In the Vietnamese Mekong delta, soil quality and crop yield are steadily declining under rice monocultures with three crops per year. The objective of this study was to evaluate the medium-term effects of rotating rice with upland crops and adding organic amendments on rice yield and relate it to soil quality. A field trial with split-plot design including two factors and three replicates was carried out from 2017 to 2020, over the course of nine consecutive cropping seasons. Crop rotations and organic amendments were applied as main-plot and subplot factors, respectively. The rotations were: 1) rice–rice–rice (R–R–R), 2) soybean–rice–rice (So–R–R), and 3) sesame–rice–rice (Se–R–R), while organic amendment treatments included: i) no amendment (NO-AM); ii) compost of rice straw and cow manure (RS+CM), and iii) sugarcane compost (SGC); the composts were applied at a rate of 2.0 t ha-1. The rotation cycle started with the so-called spring–summer (SS), followed by summer–autumn (SA) and ended with the winter–spring (WS) season. Rice yield significantly (P < 0.05) increased by organic amendments after nine growing seasons (2019–2020 WS), with an increment of 5.1% for RS+CM (7.07 ton/ha) and 6.1% for SGC (7.14 ton/ha). Contrary to our expectation, rotations with upland crops did not significantly increase rice yield. Rice yield significantly and positively correlated with an integrated soil quality index SQI (r = 0.86) for the topsoil (0–15 cm), but not for the subsoil (15–30 cm). The increased availability of soil nutrients (Si and marginally also P) and improved soil physical properties probably induced by organic amendments, along with other soil properties under study, cumulatively attributed to enhanced rice yield. Use of a SQI involving several soil quality indicators enables to overall quantify the importance of soil fertility for rice yield versus other factors and it provides an effective means of quantifying the integrated effect of improved management. Moreover, integrating a wide range of soil quality indicators in a SQI ensures its applicability across diverse settings, including different crop rotations and various soil types.

Present study on land use land cover analysis and taluka wise combination of cropped area, carried out in Beed district of Maharashtra state in India and studied 11 talukas of the district. Secondary data on land use land cover and area in various crops has been collected, compiled and analyzed from various secondary sources. Simple descriptive statistics used to examine the agricultural land use land cover and J.C. Weaver’s crop combination technique was used for taluka wise analysis of crop combinations and on the basis of highest to smallest area engaged in particular crop, crops have been ranked. The study showed that, in the district as a whole, percentage of uncultivated land other than fallow was about 46 percent as compare to net sown area is only 35 percent in total geographical area (1063855ha) of Beed district. The study further revealed that about 42 percent area engaged in food crops and about 58 percent area occupied by nonfood crops, which includes cereals (25%), cotton (19%), pulses (13%), oilseed (12%), sugarcane (5%) and fruits and vegetables (3%). Results of Weaver’s crop combination technique showed that the Wadwani taluka demarcated as most diversified taluka by means of maximum number (sixteen) of crops combinations with the lowest variance value (14) found in the present investigation. Further, the study revealed that the high crop diversity found in Dharur taluka with eighteen crop combinations and the taluka has given first rank amongst others. To derive the crop diversity ranking, taluka wise maximum to minimum number of crop combinations derived in different talukas and given ranks accordingly. The study concluded that, agricultural land use analysis is essential for solving and optimizing the land use problem, maintaining socio-ecological balance, best utilization of scarce resources. The authors enlightened that the rational use of agricultural land has important for food security and biodiversity which increase the productivity and profitability of the agriculture sector and it indicates the decision making ability of farmers in study area.

This manuscript reviews two decades of projects funded by the Kirkhouse Trust (KT), a charity registered in the UK. KT was established to improve the productivity of legume crops important in African countries and in India. KT’s requirements for support are: 1) the research must be done by national scientists in their home institution, either a publicly funded agricultural research institute or a university; 2) the projects need to include a molecular biology component, which to date has mostly comprised the use of molecular markers for the selection of one or more target traits in a crop improvement programme; 3) the projects funded are included in consortia, to foster the creation of scientific communities and the sharing of knowledge and breeding resources. This account relates to the key achievements and challenges, reflects on the lessons learned and outlines future research priorities.

High-density olive groves, despite their interesting production potential, have several limitations, also due to the high fruit load and the irrigation requirements.The study aimed to evaluate the effects of fruit load and deficit irrigation on oil yield and fruit quality, and on olive chemical composition in a high-density olive grove (cv Sikitita). Our main hypothesis was that the primary metabolism, as influenced by crop load and stress, could modify the accumulation of different phenolic classes.Different fruit loads were generated through flower thinning (66%, 50%, 33%, 0%) and two deficit irrigation treatments (-60%, -75%) were compared to the well-watered control (920 m3/ha).Thinning treatments had a limited effect on oil yield, on the other hand, deficit irrigation caused considerably less oil accumulation in the fruit on all sampling dates.Thinning 66% and deficit irrigation 75% were considered with the control for untargeted metabolomic analysis, considering three sampling dates. 233 different phenolic compounds were annotated. Multivariate HCA results indicated the impact of harvest time on the phenolic profile of olive fruits, yielding two separate clusters grouping t1 and t2 together and apart from t3. Regarding agronomic techniques, they played a differential role in the phenolic profile (supervised OPLS – DA). Fruit load mostly affected flavonoid glycosides. In contrast, the phenolic response to deficit irrigation is more heterogeneous, with phenolic acids (35%), flavonoids (25%), LMW, and other phenols (25%).

To investigate the effects of organic fertilizer combined with reduced chemical fertilizer on radish yield, quality, and soil environment in high mountainous areas (elevation: 1800 m), and to mitigate the environmental pollution caused by excessive use of chemical fertilizers. The field trial was conducted to study the effects of different fertilizer treatments. (Control:local fertilizer application rate: 375 kg·hm² reduction in chemical fertilizer + 4500 kg·hm² organic fertilizer; T1:12% reduction in chemical fertilizer + 4500 kg·hm² organic fertilizer; T2:20% reduction in chemical fertilizer + 4500 kg·hm² of organic fertilizer; T3:28% reduction in chemical fertilizer + 4500 kg·hm² organic fertilizer). Four different fertilizer ratios were studied to assess their impacts on radish quality, yield, and soil environment. The findings showed that T2 significantly increased radish yield by 12.92%. Moreover, it led to significant improvements in the content of vitamin C, soluble sugars, soluble solids, soluble proteins, titratable acidity, and glucosinolates, which increased by 10.62%, 2.15%, 50.00%, 26.90%, 43.90%, and 44.57%, respectively. Treatment T2 also significantly increased soil nutrient content, with a 7.69% and 14.29% increase in total nitrogen and total phosphorus content compared to the control (Control) treatment, and a significant reduction of 21.64% in soil bulk density . T2 treatment significantly enhanced soil urease activity, sucrase activity, alkaline phosphatase activity, and catalase activity, showing an improvement of 28.97%, 55.92%, 52.08%, and 17.47% compared to the control, respectively (p<0.05). Moreover, treatment T2 resulted in a relative abundance increase of 12.25% and 10.43% in Proteobacteria and Actinobacteria respectively. Thus, the combined application of T2:20% reduction in chemical fertilizer + 4500 kg·hm² of organic fertilizer improved the quality and yield of radish while optimizing the soil environment. T2 effectively promoted radish growth, enhanced radish yield and quality, and improved the soil environment.

The aim of this work was to describe the physiological limitations induced under drought conditions in young sweet orange trees, including the crop capability to recuperate its physiological status once re-watering, and after a water withholding. The trial was conducted during two consecutive months (July and August) in two-years old sweet orange trees. Three irrigation treatments were arranged: A full irrigation treatment; (FI); a low-frequency deficit-irrigation treatment (LFDI); and a sustained-deficit irrigation (SDI) treatment. The obtained results showed that for Stem values below -1.5 MPa, significant decreases in gas exchange rates, evidencing diffusive limitations to drought stress. Additionally, there was evidence of increased osmolyte synthesis, compatible as a defensive response to prevent oxidative damage. Likewise, the isohydric behaviour observed also resulted in significant decreases in gs even for moderate stress situations. In addition, it was observed that in the LFDI treatment, the stress levels reached caused a significant increase in the levels of Pro and MDA, which were not equal to those detected in FI during the recovery periods, which leads us to assume that potential values below -1.8 MPa would be causing significant oxidative damage with no capacity for subsequent recovery, and probably affecting the final yield.

Soybean (Glycine max L.) is a vital leguminous crop known for its rich nutritional profile, includ-ing high protein and oil content, making it a valuable resource in global agriculture and human nutrition. In Pakistan due to regulatory issues non-GMO soybean is grown therefore, the universal herbicide control method is not applicable. Similarly, for other crops, specific herbicides are avail-able but for soybean no crop specific herbicide is present. A comprehensive study was conducted over two growing seasons, in 2022 and 2023, at Agronomic Research Farm University of Agri-culture Faisalabad. The research involved 16 different treatments, including both six pre-emergence and eight post-emergence herbicides as well as a control group and weedy check. Findings of this study showed that critical importance of weed management in soybean cultiva-tion, as unchecked weed infestation can lead to substantial yield losses. Among the pre-emergence herbicide S-Metolachlor + Pendimethalin performed best and showed higher plant height (54.33 cm & 53.67 cm), 100 grain weight (11.57 g & 12 g), seed yield plant-1 (13.07 g and 12.87 g), lower Susceptibility Index (10%), higher weed control percentage (91.21% & 95.65%) higher weed con-trol efficiency (92.02% & 93.43%), herbicide efficiency index (27.54% & 32.48%) and lower weed index (18.16% & 21.70%) during 2022 and 2023 respectively. However, in post emergence herbi-cide the Fluizefop-p-butyl is most effective herbicide due to less crop injury 10% Susceptibility In-dex, higher plant height (42.67 cm & 44 cm), whole plant weight (93.33 g & 95 g), 100-grain weight (11.40 g & 11.77 g), seed yield plant-1 (12.87 g & 13.13 g), higher weed control percentage (91.12% & 91.03%) higher weed control efficiency (89.73% & 92.36%, herbicide efficiency index (21.18% & 30.41%) and lower weed index (19.42% & 20.08%) in both 2022 and 2023 respectively. Notably, herbicides such as S-Metolachlor + Atrazine + Mesotrione, Topramezone, Mesosulfu-ron-methyl + Iodosulfuron-methyl sodium and Mesotrione + Atrazine led to complete plant mor-tality, resulting lethal to soybean. The findings of this study support the hypothesis that screened the best suitable herbicides with less plant injury, and higher efficiency index. So, it is recom-mended that the application of S-Metolachlor + Pendimethalin as PRE and Fluizefop-p-butyl as POST showed the least injury and best weed control results with higher grain yield.

Genetic engineering in the field for designing crop improvement has become an essential element in responding to the increasing need for food safety, environmental sustainability, and climate-resilient crops. The latest molecular technology is used by many breeders today to achieve remarkable results. Fast genome editing is currently being seen in a variety of organisms, including plants, because to the high specificity, high effectiveness, and low production cost of CRISPR/Cas [Clustered regulatory interspaced short palindromic repeat (CRISPR)-associated protein (Cas)] technology. Its applications in developing higher-yielding, nutrition rich, disease resistance, stress-tolerant crops fruits, and vegetables. Therefore, this technology has the potential to revolutionize agriculture and contribute to global food security. The Cas protein provides the enzymatic machinery necessary for the acquisition of new cells that target invasion elements. Cas proteins such as Cas9, Cas12, Cas13, and Cas14, among others, have distinct architectures and have been used to create new genetic tools that improve features that are important for agriculture. The versatility of Cas has accelerated genomic analysis and facilitated the use of CRISPR/Cas to manipulate and alter nucleic acid sequences in cells of different organisms. This review provides the evolution of CRISPR technology exploring its mechanisms and contrasting it with traditional breeding and transgenic approaches to improve different stress tolerance. We will also discuss the CRISPR/Cas system and explore the workings of the three Cas proteins that are currently known to exist: Cas12, Cas13, and Cas14. The edited crops by this technology do not have foreign DNA inserted into their genome, therefore, they are considered as non-transgenic by the regulatory bodies in most countries.

Multi-cropping is becoming an increasingly popular approach in agriculture to tackle major and complex agroecosystem problems such as biodiversity and soil fertility losses, erosion and degradation, increased greenhouse gas emissions, etc. Comprehensive assessment of the impact of multi-cropping intensity on the agroecosystem is a new and still under-researched approach that provides a better understanding of the impact of the individual indicators studied on the overall functioning of the biodiversed agroecosystems. Data from a stationary field experiment with multi-cropping at the Vytautas Magnus University, Experimental Station between 2020 and 2022 were used to achieve this objective. The study included maize, hemp and faba bean as single, binary, and ternary crops. A complex assessment approach (CEI value) was used to determine the impact of these diversified crops on the agroecosystem, the interrelationships between the main indicators and the strength of their effects. It was found that the most positive effect on agroecosystem had ternary maize-hemp-faba bean crop. The effectiveness of other crops was from 2 to 35% less. The lowest one was calculated for the maize-faba bean crop.

The cultivation systems of Coffea spp in a cooperative society in Puebla, Mexico, include rustic, traditional polyculture, commercial polyculture, unshaded monoculture and shaded monoculture. In this work, the properties of the soil were analyzed through physical, chemical, and biological analysis to determine its nutritional status. Composite sample analyses were conducted to determine physical, chemical and microbiological parameters (fungi, actinomycetes, mesophilic bacteria, nitrifying and denitrifying bacteria). Leaf nutrients were determined. Rustic was the cropping system with the highest amount of K (0.76 cmol kg-1) in soil and nutrient assimilation in leaf (N= 2.79%, P= 660.01, K= 17297.22 and Fe= 271.24 mg kg-1) (p= 0.001); in addition to presenting high populations of mesophilic bacteria, fungi and actinomycetes (30.16, 0.59 and 0.83 respectively, x10 6 CFU g-1 soil) and very low nitrification and denitrification rates. The principal component analysis (PCA) (>3.25%) indicated that actinomycetes and K in soil favor the assimilation of Fe, K and P. This Coffea spp cultivation system had a lower impact on soil health than the rest of the systems and favored forest ecosystem conservation.

Alternative fertilizations are essential to minimize the deteriorating effects of chemical fertilizers on soil and water quality/health. Accordingly, the present work investigated the effects of combined organic-inorganic fertilization (COIF) on wheat and rice yields, soil nutrients, and soil Cd accumulation. Hence, seven different treatments were set up: control (CK); conventional fertilization (CF); optimized fertilization (OF); organic fertilizer replacing 25% (T1) and 50% (T2) of OF; and organic nitrogen (N) replacing 25% (M1) and 50% (M2) of OF-N. Overall, significant increases occurred in the yields of COIF crops. Compared with the CF, the highest wheat and rice yields happened in M1 treatment (approximately by18.5%) (P < 0.05). COIF slightly alleviated soil acidification, and improved the cation exchange capacity (CEC) of the study soils. Besides, COIF treatments significantly increased the contents of total phosphorus, total potassium, available phosphorus, and available potassium by 6.35 to 16.9 %, 3.17 to 10.9 %, 5.53 to 28.7 %, and 2.6 to 12 %, respectively (P <0.05). Nevertheless, negligible increases took place in Cd content of COIF soils compared with the CK. Altogether, our results concluded on 25% replacement of OF-N by organic N (M1) to effectively improve the fertility/ecological sustainability of the study soils.

Employing remote sensing techniques to evaluate crop residue cover (CRC) enables rapid mapping of tillage practices over large cropland areas, which has significant practical implications for monitoring the implementation of conservation tillage practices. This research utilized Sentinel-2 MSI and Landsat-8 OLI remotely sensed data, various spectral indices were calculated and the datasets were correlated with the field-measured CRC data. Selected spectral indices, highly significantly correlated, were chosen to build a correlation model between the indices and the CRC. Considering Sentinel-2 MSI and Landsat 8 OLI remotely sensed data variations in spectral and spatial scales, the accuracy of CRC models was evaluated using R2 and RMSE values. From the results, it's evident that all six spectral indices, including STI, NDTI, SRNDR, NDI5, NDI7 and NDSVI, registered correlation coefficients with the CRC exceeded 0.3 in the study area. STI and NDTI showed a stronger correlation to CRC, with coefficients of 0.810 and 0.803, respectively. For the Landsat 8 data, the lowest correlation obtained was for NDSVI with the Landsat 8 data with a correlation coefficient of 0.358. Applying the linear regression technique, STI and NDTI obtained higher accuracy compared to other indices in the Sentinel-2 MSI data. For NDTI and STI, The R2 values were 0.650 and 0.665, respectively, while their corresponding RMSE values were 3.96% and 4.03%. The highest model accuracy was obtained for the STI and NDTI constructed from the Landsat 8 OLI data (R2 values 0.41 and 0.40; RMSE values 5.25% and 5.27%, respectively). The CRC model built using partial least squares regression (PLSR) exhibited greater precision when applied to the Sentinel-2 MSI data (R2 value 0.894; RMSE value 2.18%) in comparison to the Landsat 8 OLI data (R2 value 0.695; RMSE value 3.70%). PLSR has the potential to enhance the precision of the model. In addition, the study area shows that the estimation of the CRC is better in the one-dimensional linear regression model and the PLSR model created with the Sentinel-2 MSI optical data compared to the Landsat 8 OLI optical remotely sensed data. The validation suggests that the CRC estimation in the study area is more precise when using the model relying on Sentinel-2 satellite imagery rather than the Landsat 8 OLI imagery.

A comprehensive investigation was undertaken at Vytautas Magnus University Experimental Station, located at 54°52’50” N latitude and 23°49’41” E longitude on soil (Epieutric Endocalcaric Planosol – PLe-gln-w) since 1999, to understand the impacts of different agrotechnical measures on soil health and crop yield. Two primary factors were assessed. Factor A incorporated practices of straw removal versus straw chopping and spreading, while Factor B evaluated a spectrum of tillage techniques: conventional deep ploughing, shallow ploughing, ploughless tillage, single seedbed discing, and two no-tillage practices, one of which involved cover crops. Findings from this long-term study highlight the significant potential of specific farming systems in enhancing soil organic carbon. It has a positive effect on the release of CO2 emissions from the soil, thus promoting soil resilience and increasing plant productivity. These insights are paramount in devising sustainable agricultural strategies to counter the challenges of climate change on agroecosystems. This research showcases the profound effects of combining residue management and tillage practices, setting a novel standard for sustainable soil management of climatic uncertainties.

Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. There are many studies focused on flood impact assessments for administrative districts but fewer on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and rainfall data, combined with crop growing season data to construct a crop flood damage assessment index (CFAI). First, an analytic hierarchy process (AHP) was used to assign weights to the impact factors; then, the weighted composite score method was used to calculate the CFAI; and finally, the impact was classified as slight, moderate, or severe based on the magnitude of the CFAI . This method was used for the Missouri River floods of 2019 in the U.S. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to validate the results. The combined approach achieved a high degree of consistency in spatial distribution. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.

Endophytic fungi (EF) have emerged as promising modulators of plant growth and stress tolerance in agricultural ecosystems. This review synthesizes current knowledge on the role of EF in en-hancing crop adaptation to abiotic stress. Abiotic stresses, such as drought, salinity, and extreme temperatures, pose significant challenges to crop productivity worldwide. EF have shown re-markable potential in alleviating the adverse effects of these stresses. Through various mechanisms including synthesis of osmolytes, production of stress-related enzymes, and induction of plant de-fense mechanisms, EF enhance plant resilience to abiotic stressors. Moreover, EF promote nutrient uptake and modulate hormonal balance in plants, further enhancing stress tolerance. Recent ad-vancements in molecular techniques have facilitated the identification and characterization of stress-tolerant endophytic fungal strains, paving the way for their utilization in agricultural prac-tices. Furthermore, the symbiotic relationship between EF and plants offers ecological benefits, such as improved soil health and reduced dependence on chemical inputs. However, challenges remain in understanding the complex interactions between EF and host plants, as well as in scaling up their application in diverse agricultural systems. Future research should focus on elucidating the mechanisms underlying endophytic fungal-mediated stress tolerance and developing sustainable strategies for harnessing their potential in crop production.

Breadfruit [Artocarpus altilis (Parkinson) Fosberg] is recognized as a tropical fruit tree crop with great potential to contribute to food and nutrition security in the Caribbean and other tropical regions. However, the genetic diversity and identification of germplasm in the Caribbean and elsewhere is not well understood and documented. This hampers the effective conservation and use of the genetic resources of the tree crop for commercial activities. This study assessed the genetic identity, diversity, ancestry, and phylogeny of breadfruit germplasm existing in Caribbean and recently introduced accessions using 117 SNPs from 10 SSR amplicon sequences. The results showed that there was high genetic diversity in the breadfruit germplasm in the Caribbean, as well as the newly introduced breadfruit accessions based on nucleotide diversity (π_T) and nucleotide polymorphism (θ_W). Furthermore, based on these measures of genetic diversity, there were no significant differences between existing Caribbean breadfruit accessions and newly introduced breadfruit accessions which are known to be highly diverse. Ancestry and phylogeny analysis corroborated the genetic relatedness of these two groups with accessions of these groups being present in both main germplasm clusters. This suggests that the existing Caribbean breadfruit germplasm harbors a higher level of genetic diversity than expected.

Côte d’Ivoire has substantially neglected crop residues from farms in rural areas. This study aims to provide strategies for the sustainable conversion of crop residues to hydrogen. Using existing data and theoretical approaches, up to 16,801,306 tons of crop residues of 11 crop types were available in Côte d’Ivoire in 2019. A theoretical hydrogen potential of 1,296,424.84 tons was estimated from the crop residues. Technically, 907,497.39 tons of hydrogen is expected annually. Three scenarios of hydrogen project implementation developed indicated that the Ivorian industries can be supplied with 9,026,635 gigajoules of heat. Moreover, 17,910 cars and 4,732 buses can be supported in the transport sector. It is estimated that 817,293.95 tons of green ammonia for farmers can be supplied. Five million, seven hundred twenty-seven thousand, nine hundred ninety-two (5,727,992) households are expected to have access to 1,718.40 gigawatts of electricity. Due to these changes in transport, energy, industry, and agricultural sectors, a reduction of 1,644,722.08 tons of carbon dioxide per year can be achieved theoretically. With the scenarios, some 263,276.87 tons of hydrogen are exportable to other countries. Conversion of crop residues to hydrogen is a promising opportunity with environmental and socio-economic impact. Therefore, this study requires further extensive research.

The storm 'Daniel' has caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by extensive soil sampling and laboratory analysis. Causal relationships between soil chemical properties and sediment deposition were extracted using the DirectLiNGAM algorithm. Results of causality analysis showed that the sediment deposition affected the CaCO3 concentration in the soil. Also, causal relationships were identified between CaCO3 and available phosphorus (P-Olsen), as well as between sediment deposit depth and available manganese. The quantified relationships between the soil variables were then used to generate data using a Multiple Linear Perceptron (MLP) regressor for various levels of deposit depth (0, 5, 10, 15, 20, 25 and 30 cm). Then linear regression equations were fitted across the different levels of deposit depth to determine the effect of deposit depth on CaCO3, P, and Mn. The results show that there is a linear slope of 0.12, -0.16, and 0.13, for CaCO3, P, and Mn with deposit depth, respectively. Statistical analysis indicates that corn growing in soils with sediment over 10 cm requires a 31.8% increase in P rate to prevent yield decline. Additional notifications regarding cropping strategies in the near future are also discussed.

Insurance serves as a mechanism to effectively manage and transfer revenue-related risks. We conducted a study to explore the potential financial advantages of index insurance, which protects agricultural producers, specifically sugarcane, against excessive rainfall. Creation of the index involved utilizing generalized additive regression models, allowing for consideration of non-linear effects and quantile generalized additive regression to evaluate relationships with lower quantiles, such as low yield events. To quantify the financial benefits for farmers, should they opt for excessive rainfall index insurance, we employed efficiency analysis based on metrics such as Conditional Tail Expectation, Certainty Equivalence of Revenue, and Mean Root Square Loss. The results of the regression model demonstrate its accuracy in predicting sugar cane yields, with a split testing R2 of 0.691. Additionally, our study suggests that this type of insurance could also apply to sugarcane farmers and other crop producers in regions where extreme rainfall threatens the financial sustainability of agricultural production.

Although crop conversion from annual to perennial crops has been considered as one path towards climate-smart and resource-efficient agriculture, the effects of this conversion on soil multifunctionality and biomass yields remain unclear. Here, we investigated the effects of one annual and three perennial crops (a grass (Lolium perenne), a legume (Medicago sativa), and their mixture) on soil multifunctionality and biomass yield on the Yellow River floodplain. Soil multifunctionality was assessed by the capacity of water regulation and the multifunctionality of carbon (C), nitrogen (N), and phosphorus (P) cycles. Results showed that perennial crops enhanced soil multifunctionality by 207% for L. perenne, 311% for M. sativa, and 438% for L. perenne + M. sativa, compared with annual winter wheat (Triticum aestivum). The effect of perennial crops on soil multifunctionality increased with infiltration rate, dissolved organic C, microbial biomass C, and extracellular enzymatic activities for both C and N acquisition. However, we observed that perennial crops had lower biomass yield than annual crop. Therefore, the transition of agricultural landscapes to perennials needs to take into account the balance between environmental protection and food security, as well as environmental heterogeneity, to promote sustainable agricultural development.

Agriculture is the main driver of depletion resources worldwide, and its duty is to ensure food security within a rapidly increasing demographic and urbanization, so it is important to transition to sustainable production systems. Vertical crops (VCs) can reduce the pressure on conventional agriculture because they save water and nutrients and increase crop yield. Therefore, this study aimed to validate a proposed predictive model (PM) to simulate water and nutrient uptake in vertical crops under greenhouse conditions. Based on the Penman-Monteith equation, PM estimates transpiration, while nutrient uptake was estimated using the Carmassi-Sonneveld submodel. PM was experimentally evaluated for vertically grown lettuce under Mediterranean greenhouse conditions, during spring 2023. The irrigation technique was a closed-loop fertigation circuit. The experimental consisted of testing two densities (50 and 80 plants·m-2), where each unit of the experiment unit was divided into three heights (low, medium, and upper). It performed ANOVA with a value of p < 0.05 and R2 to assess PM performance. The results suggest a high degree of PM, since R2 ranged from 0.7 to 0.9 for the uptake of water and nutrients. Both densities had a yield between 17-20 times higher than conventional lettuce production and significant savings in water, between 85-88%. In this sense, PM has great potential to intelligently manage VC fertigation, saving water and nutrients, which represents an advance towards reaching SDG 6 and SDG 12, within the 2030 Agenda.

There is a growing concern about climate change being a serious threat to insurance business sustainability. Knowing if climate warming is a cause for claims and losses increase, and how this cause-effect relationship will develop in the future are two open major questions. In this article, we answer both questions by particularizing the geographical area of Spain, and a precise risk, hailstorm in crop insurance in the line of business of wine grapes. We measure climate change thanks to the Spanish Actuarial Climate Index (SACI). We use a database containing all the claims caused by hail risk in Spain from 1990 to 2022. With homogenized data, we consider as dependent variables the monthly number of claims, monthly number of loss costs equal to one, and monthly total losses. The independent variable is the monthly Spanish Actuarial Climate Index (SACI). We try to explain the former through the latter using regression and quantile regression models. Our main finding is that climate change as measured by SACI explains these three dependent variables. We also give an estimation of the increase of the monthly total losses Value at Risk, corresponding to a future increase in climate change measured in units of SACI. Spanish crop insurance managers should carefully incorporate these conclusions into their decision-making process to keep this line of business sustainable in the future.

The use of biostimulants consisting of plant growth promoting rhizobacteria (PGPR) is rapidly expanding in horticulture in recent years. In the current study, a novel mix of six Bacillus sp. strains was tested as a PGPR biostimulant in two experiments with zucchini squash (Cucurbita pepo L.) cultivated in the greenhouse soil and in an open field, respectively. In both experiments, seeds of the local landrace ‘Kompokolokytho’ and the commercial hybrid ‘ARO-800’ were either inoculated or non-inoculated with the PGPR biostimulant. The application of the six Bacillus sp. strains increased both the vegetative growth and the yield of zucchini squash, and these effects were associated with significantly higher shoot phosphorus levels in both experiments and both genotypes. Furthermore, at the end of the cultivation, the colony forming units of Bacillus sp. were appreciably higher in plants originating from inoculated compared to non-inoculated seeds, indicating that the tested mix of Bacillus sp can be successfully applied through seed inoculation. ‘ARO-800’ produced more vegetative and fruit biomass than ‘kompokolokytho’ under greenhouse cropping conditions, while in the open-field crop both genotypes performed equally, presumably because the local landrace is traditionally grown in open fields, and thus it is not well adapted to greenhouse cropping.

Agricultural intensification increased crop productivity but simplified production with lower diversity of cropping systems, higher genetic uniformity, and a higher uniformity of agricultural landscapes. Associated detrimental effects on the environment and biodiversity as well as the resilience and adaptability of cropping systems to climate change are of growing concern. Crop diversification may stabilize productivity of cropping systems and reduce negative environmental impacts and loss of biodiversity, but a shared understanding of crop diversification including approaches towards a more systematic research is lacking. The current review highlights the use of Cropping Systems Diversification as an Approach to Enhancing Crop Productivity. Crop diversification can be considered as an attempt to increase the diversity of crops through, e.g. crop rotation, multiple cropping or intercropping, compared to specialized farming with the aim to improve the productivity, stability and delivery of ecosystem services. It can be one measure to develop more sustainable production systems, develop value-chains for minor crops. Crop diversification practices can include higher crop diversity, more diverse crop, mixed cropping; cultivation of grain legumes in otherwise cereal dominated systems, perennial leys or grassland and regionally adapted varieties or variety mixtures. Crop diversification and/or additional diversification measures like variation of seeding time or changing cropping patterns have the potential to lead to higher and more stable yields, increase profitability and lead to greater resilience of agro-ecosystems in the long term. These practices have the potential to make cropping systems more diverse in space, time and genetics. Through a balanced portfolio approach to agricultural sustainability, cropping system performance can be optimized in multiple dimensions, including food and biomass production, profit, energy use, pest management, and environmental impacts.

The effect of climate change plays a significant role on groundwater level variations and crop yield. The change in climate leading to increased temperatures, decreased rainfalls and extreme drought conditions ultimately cause low groundwater level and crop yield. Judicious groundwater management technology viz., water recharge options, need based irrigation, crop selection, must be adapted for further increasing the water table level in soil ecosystem. Additionally, climate change impacts combined with changes in agricultural water use can affect groundwater dynamics. Increased irrigation demand and decreased summer precipitation can lower groundwater levels, impacting crop production. Overall, climate change affects both groundwater resources and crop yield highlighting the need for sustainable water management practices and consideration of soil properties in agricultural modelling. The authors carefully selected relevant research articles addressing the impact of climate change on ground water level in Asian countries. The review highlights the use of machine learning method to ground water level and crop yield modelling. According to the study, machine learning techniques have made significant contributions to predicting groundwater level changes and crop yield with higher accuracies, high performance and less running time.

Soil moisture reserve is a key factor in maintaining soil fertility and all other related ecosystem services (including carbon sequestration, soil biodiversity, soil erosion control). In semiarid blown-sand areas under aridification water preservation is a particularly crucial task for agriculture. The international Diverfarming project (2017–2022) within the EU Horizon 2020 Program focused on the impacts of crop diversification and low-input practices in all pedoclimatic regions of Europe. In the three-year experiment conducted in the Pannonian region the impacts of intercropping asparagus with different herbs on some provisioning and regulating ecosystem services were evaluated in the Kiskunság sand regions. Relying on the findings based on a range of measured physical and chemical soil parameters and on crop yields and qualitative properties, advice was formulated for farmers. The message drawn from the experiment is somewhat ambiguous. The local farmer agrees that crop diversification improves soil quality, but he denies that it would directly influence farm competitiveness, which primarily depends on cultivation costs (such as fertilization, plant protection and labour). Further analyses are needed to prove the long-term benefits of diversification through enriching soil microbial life and the possible reduction of fertilizer use while water demand is kept at a low level and the same crop quality is ensured.

The impact of long-term soil and crop management practices on soil biological and chemical properties in oilseed brassica-based production systems were deliberated for holistic soil health improvement in the present study. Different tillage, crop residue re-cycling and cropping systems were studied for 5-years in 0-15 and 15-30 cm soil profiles. No-till permanent beds with crop residue (PB+R) noticeably improved soil organic carbon (SOC), microbial biomass carbon (MBC), enzymes [dehydrogenase (DHA) and alkaline phosphatase (AlP)], nitrogen fractions (available, nitrate and total), and available phosphorus and potassium content in both the soil layers compared to conventional tillage without crop residues. Though, the plow soil layer (0-15 cm) showed higher concentration of soil carbon, enzymes, N fractions and available P than in the sub soil (15-30 cm). The dynamic soil biological and chemical properties were also varied with the crop stage, and recorded higher MBC at 30 days, SOC and enzymatic activities at 60 days, and N fractions and available P and K at harvest of mustard crop. Green gram-mustard rotation showed higher values of biological and chemical parameters. Thus, the legume-based mustard crop rotation following no-till permanent beds and residue re-cycling found holistic in improving soil health, and nutrient cycling.

Mesoporous silica nanoparticles (MSNs) can be used as carrier materials for the controlled release of pesticides while reducing their negative environmental impact. In this study, we screened an active ingredient, p-cymene (PC), with an excellent inhibitory effect on rice bacterial blight. Subsequently, the PC was successfully loaded onto MSNs via physisorption(PC@MSNs). PC@MSNs, characterized by a regular spherical shape, smooth surface, and an MSN average size of 262.9 nm, achieved an 8.6 % drug loading capacity. The release kinetics of the PC from the PC@MSNs demonstrated a sustained release (288 h) pattern influenced by drug diffusion. The efficacy of the PC@MSNs against Xanthomonas oryzae pv. Oryzae paralleled those of PC. Acute toxicity assays re-vealed that the PC@MSNs were less toxic to aquatic life (LC50=257.867 mg/L) and that the formu-lation showed no adverse effects on rice seedling growth. In summary, these results suggest that PC@MSNs can broaden PC's application scope in managing rice diseases.

In sustainable cultivation technologies, the method of managing crop residues and the microbiological activity of the soil supported by the application of effective microorganisms are of particular importance. Therefore, a four-year two-factor experiment was carried out with spring wheat monoculture. Six ways management of organic matter before sowing and tillage (first factor) and the application of microbiological preparations (second factor) were tested - a total - 18 experimental objects. Parameterized: weed infestation, SPAD and LAI indicators, elements of the yield structure and spring wheat yield - all features were significantly determined by the tillage technology used. In most cases, the application of biopreparations does not have a significant impact on the tested features. It was found that the highest yields of spring wheat obtained in the technology: application of EM or UGmax microbiological preparations on the shredded straw of the forecrop and mixing it with the soil using a grubber immediately after harvest, sowing of the white mustard catch crop and winter plowing.

Biological control, a well-established plant protection method, has garnered substantial attention in recent decades. Various approaches, including biological control agents (BCA), catch crops, biofumigation, sticky traps, and pheromones, have been extensively explored. While the effectiveness of these methods varies depending on specific circumstances, their collective significance has grown amid mounting pressures to curtail or eliminate conventional synthetic plant protection products. Previous review articles have highlighted the synergistic benefits of using two or more BCAs simultaneously, yet limited information exists regarding the concurrent use of diverse biological control methods. This comprehensive review incorporates a thorough literature search to assess the synergistic potential of concurrently employing two or more of these methods, followed by a discussion on perspectives of holistic management and mimicking of complex natural systems, shedding light on the vast potential and need for further research in this domain.

Leaf area estimation is a critical component in the study of plant growth and productivity within agricultural systems. This research introduces the LeafArea package, a specialized tool designed to calculate the leaf area of six distinct Andean fruit species: S. quitoense, S. betaceum, P. peruviana, R. fruticosus, P. ligularis and P. edulis. Leveraging response variables such as species type, leaf length and width, the package employs advanced machine learning algorithms to estimate leaf area accurately. The primary focus of the study is to identify the most effective model for describing the relationship between leaf width, length, and area for each plant species. Currently, the LeafArea package utilizes four different machine learning algorithms, namely generalized linear model (GLM), generalized linear mixed model (GLMM), Random Forest and XGBoost. Among these, XGBoost stands out as a top-performing algorithm, exhibiting exceptional predictive accuracy. The evaluation metrics employed in the program provide valuable insights for researchers, aiding in informed decision-making. Specifically, XGBoost demonstrates significantly lower prediction errors and approaches a near-perfect R2 value, emphasizing its potential to enhance predictive accuracy. These results underscore the efficacy of machine learning techniques, as a compelling choice for researchers seeking precise and robust predictions in leaf area estimation. The LeafArea package thus represents a valuable tool for advancing our understanding of plant growth dynamics, resource allocation, and overall productivity within agricultural ecosystems.

The objective was to estimate the correlation between VIs and grain yield and identify the optimal timing and VIs for precise corn grain yield estimation. Furthermore, the study aims to employ photographic quantification to measure corn ear traits and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in CRB with three rep-lications at three locations. Vegetation indices and green leaf area were estimated throughout the cycle using an unmanned aerial vehicle (UAV) and subsequently corre-lated with grain productivity. In addition, photographs were taken of the corn ear to esti-mate their length, width and total number of kernels and compare these values with manual measurements. The experiments consistently demonstrated significant experi-mental quality across sites, with accuracy ranging from 79.07% to 95.94%. UAV flights carried out at the beginning of the crop cycle revealed a positive correlation between grain productivity and the evaluated indices (NGRDI, VARI, GLI). Regarding the phenotyping of corn ears, the regression coefficients for width, length and TNG were 0.92, 0.88 and 0.62, respectively, indicating an association with manual measurements. However, stage V5 in the localities of Lavras and Ijaci and stage V8 in the locality of Nazareno showed a posi-tive correlation with productivity. The use of images for ear phenotyping is promising as a method for measuring corn components.

Smart farm is a technology that greatly helps improve the productivity and quality of crops and is being actively introduced into indoor environments such as green houses. However, it has not yet been distributed to field-grown crops. The main reason is that, unlike indoor environments, it is very difficult to collect sensor data for monitoring the growth environment of crops in open fields where weather conditions change significantly and power supply is difficult. Additionally, because various sensors are used, the data formats of the devices are different, making it difficult to process. This paper presents a field crop growth environment monitoring and big data analysis system. The proposed system first solves power supply and data communication problems using solar power generation and LPWA technology. Additionally, based on the oneM2M architecture, data from various sensors is transmitted to the server using standardized technology. The transmitted data is stored and managed on the server as big data, and can be used to predict the production and quality of field-grown crops and take appropriate measures. The proposed system is expected to create an environment optimized for the growth of field crops and help prevent and manage diseases.

Studies on the domestication, genetic differentiation, and crop evolution of foxtail millet are re-viewed. Several genetic studies were carried out to elucidate the genetic relationships among foxtail millet accessions originating mainly from Eurasia, based on intraspecific hybrid pollen semi-sterility, isozymes, DNA markers, and single-nucleotide polymorphisms. Most studies suggest that China is the center of diversity of foxtail millet, and landraces were categorized into geographical groups. Furthermore, the evolution of genes was reviewed (i.e., the Waxy gene conferring amylose content in the endosperm, the Si7PPO gene controlling polyphenol oxidase, the HD1 and SiPRR37 genes controlling heading time, the Sh1 and SvLes1 genes involved in grain shattering, and the C gene controlling leaf sheath pigmentation), and the variation and distribu-tion of these genes suggested complex patterns of evolution under human and/or natural selec-tion.

Cacao (Theobroma cacao L.) is an important woody species for production in Ecuador. Woody species have the potential to incorporate soil organic carbon (SOC) into the soil. The province of Manabí is known for its cocoa plantations and several cocoa species, which generates interest in their analysis. This study aimed to determine the concentration and accumulation of SOC in three cocoa species (EET95, EET103 and EET116) 15 years old, both under the tree and in the rows, at different depths 0-5, 5-10, 10-20 and 20-30 (cm). The results showed that soil SOC concentration was higher in the row for EET116 (2.51%) and significantly different under the tree for EET103 (1.57%) at 0-5 cm. No significant differences were found in the other sections. In the whole profile analyzed (0-30 cm), the EET116 species accumulated 41 t ha-1 and 54 t ha-1 under the tree and in the row, respectively. The species EET103 in the row presented 47 t ha-1 with no significant differences. Species EET95 and EET103 had the lowest values, but without significant differences between sampling locations, with 36 t ha-1, 37 t ha-1, and 47 t ha-1 under the tree and in the windrow, respectively. In conclusion, the species EET116 accumulated more organic carbon due to several factors, possibly a higher decomposing biomass, not only from the tree but also from other trees in the area, which contributes to the incorporation of organic matter throughout the soil profile.

Agricultural productivity is expected to decrease under changing climate conditions that correspond to the stability of West African food systems. Although numerous studies evaluated food production gaps or impacts of climate variability on crop yields, many uncertainties are associated with climate extremes, but also rapid population growth and the corresponding dietary lifestyle determine farming activities. Here, we present a food supply and demand analysis based on the relationship between climate change, crop production, as well as population growth in three experimental sites from southwestern Burkina Faso to southwestern Ghana. The method consists of a tiered approach that encompassed a statistical analysis of historical climate as well as agricultural time series using boxplots and a trend test by Mann-Kendall. Food balance sheets were calculated by estimating the demand using a population growth model linked to food supply with local consumption patterns. Results of the boxplot analysis revealed for the majority of climate time series light to strong right-skewed distributions with either a few heavy rainfall or hot temperature events. We found almost insignificant rainfall and temperature trends for both sites in the Sudano-Guinean savannah. The rainfall regime of southwestern Ghana indicated an insignificant upward or downward trend while the temperature significantly increased over time. Crop yield boxplots demonstrated mainly right-skewed distributions for cereals, legumes and tubers as well as cash crops such as cotton. Backed by the Mann-Kendall test, maize and sorghum significantly enhanced in both study areas of the Sudano-Guinean savannah. Southwestern Ghana depicted a different crop pattern where cassava and plantain showed a strong upward trend at a confidence level of 1%. The grouped food balances sheets across the regions illustrated a surplus for the Sudano-Guinean savannah ranging from 221.2 megatons (Mt) to 4,846.77 Mt while southwestern Ghana exhibited a deficit between -193.33 Mt and -16,188.82 Mt. Despite a growing yield of various crops, food demand outpaces the regional production. Hence, it is recommended to focus on a larger array of commodities, imports and marketing of farm products.

Climate change and the increasing frequency of high-temperature events are significant threats to global crop yields. To address this, a comprehensive understanding of how plants respond to heat shock (HS) is essential. Signaling pathways involving calcium (Ca2+), a versatile second messenger in plants, encode information through temporal and spatial variations in ion concentration. Ca2+ is detected by Ca2+-sensing effectors, including channels and binding proteins, which trigger specific cellular responses. At elevated temperatures, the cytosolic concentration of Ca2+ in plant cells increases rapidly, making Ca2+ signals the earliest response to HS. In this review, we discuss the crucial role of Ca2+ signaling in raising plant thermotolerance and we explore its multifaceted contributions to various aspects of the plant HS response.

Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum Tuberorsum L.) crops. In this paper, we present a mobile application for potato crop pest detection based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes. An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.76%, with MobileNetv2. Based on the results obtained, an offline mobile application was implemented, supported on devices with Android 4.1 or later, also featuring an information section on the 27 pests affecting potato crops and a gallery of symptoms. For future work, segmentation techniques will be used to highlight the damaged region in the potato leaf by evaluating its extent and possibly identifying different types of pests affecting the same plant.

The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, a slow adoption of sustainable farming, a need for climate-resilient food systems, resource inequity, and protection of the small-scale farmers’ practices, all issues integral to food security and environmental health. Remote sensing technologies can assist precision agriculture to effectively address these complex problems, by providing farmers with a high-resolution lens. The use of vegetation indices (VIs) is an essential component of remote sensing, which combine the variability of spectral reflectance value (derived from remote sensing data) with the growth stage of crops. Currently a wide array of VIs is available that could be used to provide a classification and an evaluation of the state and health of crops. However precisely this high number leads to difficulties in selecting the best VI and combination of VIs for a specific objective. Without a thorough documentation and analysis of appropriate VIs, users might be confronted with difficulties in using remote sensing data or even with a very low accuracy of the results. Thus, the objective of this review is to conduct a critical analysis of the existing state of the art on the most important features related to the effective use of VIs for the discrimination and monitoring of the most important agricultural crops (wheat, corn, sunflower, soybean, rape, potatoes, and forage crops), grasslands and meadows. This data could be highly useful for all the stakeholders involved in agricultural activities (from farmers, researchers up to institutions dealing with the centralization and monitoring of agricultural crops).

Nano-nitrogen fertilizers (NNFs) have emerged as a promising technology in the field of agriculture, offering potential solutions to improve nutrient uptake efficiency, enhance crop productivity, and reduce environmental impacts. The NNFs showed superior characteristics and performance on crops and therefore, became a potential alternative to conventional nitrogen (N) fertilisers. These fertilizers enhance plant uptake while simultaneously reducing environmental losses. Additionally, their small particle size increases crop acclimation and decreases the application rate. With all these beneficial traits of NNFs, they potentially contribute to achieving Sustainable Development Goals (SDGs). This review article summarises the materials used in NNFs formulation, methods of preparing NNFs, and their crop responses. This review also highlights the limitations identified in the research studies and provides research recommendations for the future. Further, this review provides a critical assessment of the current state of NNFs and their prospects for revolutionizing modern agriculture to attain SDGs.

Screening of pollen traits in diploid wild potatoes (Solanum sect. Petota, Solanaceae) is desirable to develop heat-tolerant potato (S. tuberosum) cultivars. To accomplish this goal requires exploring potato genetic resources that are conserved in genebanks. The goal of this study was to assess pollen viability and 2n pollen production of the diploid potato wild relatives under heat stress condition. We assessed pollen viability and size of nine potato accessions conserved at the Embrapa Potato Gene Bank, including S. chacoense (BRA 00167447-2, BRA 00167017-3, BRA 00167023-1, BRA 00167028-0), S. commersonii (BRA00167007-4, BRA00167420-9, BRA00183760-8), and S. malmeanum (BRA 00183755-8), along with a control accession from the cultivated species S. tuberosum (BRA 00167251-8). The plant accessions were cultivated in different growth chambers, simulating both control temperature (ranging from 14 to 27°C) and supraoptimal temperature conditions (ranging from 24 to 34°C). At heat stress, the accessions BRA 00167251-8 did not bloom, and BRA 00167023-1 did not produce pollen. The remaining accessions did not exhibit a significant reduction in pollen viability as the temperature increased. Pollen viability at the control temperature had the lowest value in BRA-00167420-9 (S. commersonii) with 68.5% and the highest in BRA 00183755-8 (S. malmeanum) with 100%. At the supraoptimal temperature the lowest value was in BRA 00167420-9 (S. commersonii) with 54.5% and the highest in BRA 00183755-8 (S. malmeanum). The average of pollen size was 20 μm in all wild potato genotypes, and the increase of temperature did not lead to 2n pollen production. Estimated Genotypic Coefficient of Variation (GCV) was lower than Phenotypic Coefficient of Variation (PCV) for pollen viability. The observed heritability values ranged from 58.82% in BRA00167007-4 to 91.32% in BRA 00183755-8. Our results highlight the genetic variability available in wild potato germplasm concerning pollen viability under heat stress. Furthermore, these first insights offer valuable guidance for ongoing and future endeavors in diploid potato breeding.

Ultrasonic feedback energy is affected by the variety, planting and growth state of crops, it is difficult to fine application in precision agriculture systems. To this end, an ultrasonic sensor was mounted in a spray boom height detection system. Winter wheat was used as the test object to obtain feedback energy values for the spray boom height from the top of the wheat in the field during six critical growth stages: the standing stage, jointing stage, booting stage, heading stage, filling stage and maturity stage. The relationship between the actual value of the height from the spray boom to the top of wheat (Habw) and the detected value of the height from the spray boom to the top of wheat (Hdbw) was analyzed. A spray boom height detection model based on the ultrasonic sensor during the full growth cycle of wheat was determined. Field validation tests showed that the applicability of the spray boom height detection distance (Dd) of the spray boom height detection model proposed in the present study was 450 mm~950 mm. Within the applicable Dd range, the detection error of the detection model was ≤5 cm during the full growth cycle. This study provides an ultrasonic detection model construction method, which improves the reliability and accuracy of ultrasonic boom height detection for different wheat growth stages.

In this study, we examine the possibility of proactive floodwater diversion to fields via backwater in numerical experiments using multiple elevation data products with different spatial resolutions and explore the optimal timing of water diversion from the perspective of crop calendars. First, a comparison of the elevation data products revealed that photogrammetric data can capture microtopography, such as the footpaths between rice paddies and drainage channels around a field. Numerical experiments using two elevation data products, 5mDEM and LP-derived approximately 5mDEM, revealed that floodwater diversion can reduce the peak to about 19.2 m3/s when using the 5mDEM and can reduce the peak to 16.0 m3/s when using the LP-derived approximately 5mDEM. Interviews with land improvement districts and a review of previous studies revealed that the ear-burst period is particularly vulnerable to flooding. Although the effect of flood peak reduction is reduced due to flooding of the field, it is possible that floodwater can be channeled during the ripening period in August and in late September and October when the ears have been harvested.

TThe following three objectives were tested in this study: (1) investigate the utility of low-altitude remote sensing using UAS technology to compare the effects of different N application systems in rice production; (2) use spatial extrapolation to scale up plot-level generated to farmer field rice yield data based on crop spectral signatures, and (3) predict and map out rice productivity as a function of N placement systems. Images were captured on a UAV platform at midseason of the rice crop. Orthomosaics were developed for selected fields in rice-producing zones. Grain yields were assessed from low, medium, and high crop health plots delineated based on NDVI values. On the plot scale, UDP outyielded non-UDP by 0.84%. Individual plot yield data were scaled up to the farmer field level through Jenks natural breaks classification and es-tablishing an empirical relationship between OSAVI and plot yields. Assessment of the scaled-up field levelfield-level data also confirmed the superiority of UDP N man-agement over the non-UDP systems in promoting rice yields. Scaling up plot scale da-ta to whole field levels also facilitated generating and mapping expected yield maps for individual farmer fields in the three zones studied. This study has established a tangible simple but tangible protocol protocol for predicting and mapping rice yields in small-scale farmer fields using UAS data.

Japanese plums form part of a multi-billion rand deciduous fruit industry in South Africa. Despite this, there is a paucity of knowledge on the seasonal water requirements of plum orchards. In a time of changing climatic conditions and diminishing water resources, this gap in literature poses an imminent threat to the long-term sustainability and global competitiveness of the South African plum industry. Therefore, this paper aimed to provide a review of the available literature on the crop water requirements of full-bearing well-irrigated Japanese plum orchards for improved agricultural water management. Full-year water requirements for well-watered full-bearing Japanese orchards ranged between 921 and 1 211 mm a-1 with a mean value of 1 084 ±140 mm a-1. Canopy growth and pruning appeared to be the most common causes of differences in water requirement estimates. Growing season length also plays a role with late-season maturing orchards having higher water requirements than their early and mid-maturing counterparts. The knowledge review provided benchmark figures for the annual water requirements of Japanese plums. However further research is required to determine the water requirement of plums from planting to full-bearing age and the response of plum trees to water stress, in a South African context.

Abstract: A two-year study of pearl millet-groundnut cropping systems across two fertilizer levels was conducted at the INRAN/TARNA research station in Maradi, Niger in the Sahelian zone of West Africa. The objective of this study was to identify the best cropping system with and without fertilizer application to optimize pearl millet and groundnut yields, land use efficiency, and economics as measured by value-to-cost ratio. The experiment was conducted with three replications, and ten treatments combination of five pearl millet and groundnut cropping systems (pearl millet and groundnut sole crops, and three pearl millet – groundnut intercrops), and two fertilizer levels. Sole cropping produced the highest pearl millet grain by over 300 kg ha-1 and groundnut pod yields by over 500 kg ha-1, but all the intercropping systems had a LER above 1.0 with land use efficiency increased by 19 to 41%, also intercropping increased diversity, and reduced risk. Fertilizer application increased yields in all cropping systems by 200 to 600 kg ha-1. Government subsidy increased the value-to-cost ratio by 0.5 to 2, 5 units, and was required for economic response for pearl millet sole and intercrops in 2022. The groundnut sole cropping system had the greatest economic response to fertilizer application. Based on the risky environment and multiple end uses needed by producers, the intercrop system M-G: 1:3:1 with fertilizer application is usually the best option to optimize pearl millet and groundnut production.

Plant Growth Promoting Rhizobacteria (PGPR) is beneficial microorganisms that inhabit the rhizosphere of plants and play a crucial role in enhancing plant growth and development. This review provides an overview of the significant role of PGPR in sustainable agriculture. The mechanisms that promote plant growth, such as nitrogen fixation, phosphate solubilization, and growth hormones, are discussed. The potential of PGPR in improving plant tolerance to biotic and abiotic stresses, reducing the use of chemical fertilizers and pesticides, and enhancing nutrient availability, soil fertility, and uptake is also highlighted. Moreover, the ecological and economic benefits of using PGPR for sustainable agriculture include improving crop productivity, reducing environmental pollution, and ensuring food security.

The importance of irrigation in boosting agricultural production and productivity is widely understood. Proper planning and effective and efficient management and operation of irrigation system require crop and site-specific data. Since the last few years, Ethiopia has embarked on expansion of irrigated wheat. However, crop-specific data useful for irrigation planning and management for wheat crop is limited. The objective of this research was to determine water requirements and crop coefficients for wheat crops based on field measurements. The research was carried out at the Debre Zeit Agricultural Research Center in central Ethiopia during the dry season (Bega) of 2021/22. The crop considered for the experiment was durum wheat of the Utuba variety. Two non-weighing lysimeter units were employed to measure the water balance components. The soil moisture was monitored using gravimetric method on daily basis conducted both before and after each irrigation event at various depth intervals. The crop required a total of 115 days to fully mature. The length of the growth of initial, development, mid, and late stages were about 20, 30, 42, and 23 days, respectively. Microsoft Excel was used for the data analysis and the CropWAT8.0 model to estimate the reference evapotranspiration. Crop evapotranspiration (ETc) was determined using the water balance equation, and crop coefficients were computed from ETc and reference evapotranspiration (ETo). The results indicate that the total seasonal water requirement of the crop was 392.75mm. The growth stage-based water requirement was 40.35 mm, 82.44 mm, 238.66 mm, and 31.3 mm during the initial, development, mid, and late growing stages, respectively. For the initial, development, mid, and late stages of wheat growth, the crop coefficient was calculated to be 0.51, 0.83, 1.29, and 0.52, respectively. It was found that a fourth-degree polynomial equation fits well and explains the relationship between day of growth stage and kc of the crop. Although a one-season measurement, the results generated here could be useful for crop-specific data in scarce areas.

This academic paper explores the synergistic potential of Agroforestry systems and Climate-Smart Agriculture (CSA) in enhancing climate change adaptation strategies. Focusing on Australia and Pacific Island Countries, the study delves into the pivotal role of these approaches in mitigating the impacts of changing climate variables on agricultural production. By analysing important climate variables, their potential alterations, and their subsequent effects on crop production, the paper advocates for a holistic and forward-thinking approach to sustainable agriculture in the face of climate change.

The use of plant-based and microorganism-based biological inputs is a sustainable agricultural practice, promoting a suitable and better utilization of non-renewable resources in the environment. The benefits of using microorganisms are associated with both direct and indirect mechanisms, primarily related to improvements in nutrient absorption and availability, as well as plant growth. The main benefits of using biochemical pesticides are the promotion of sustainability and the management of resistance. Although the use of microorganisms and botanical metabolites is a promising agricultural alternative, it is still primarily concentrated in grain crops. However, due to the wide range of mechanisms of action of plant-based and microorganism-based biological inputs, there is an opportunity to expand their use in agriculture. In the Brazilian biological inputs market, various terminologies have been used to characterize biological-based products, with many of these terms being employed incorrectly and sometimes conflicting with the country’s legislation. Therefore, this review presents the classes of biological inputs and the application and evolution of the plant-based and microbiological-based product market in different crops in Brazil.

This review article explores the significance of beneficial microorganisms in crop improvement, highlighting their roles in promoting plant growth, enhancing nutrient uptake, and protecting against pathogens. The symbiotic interactions between plants and microorganisms have been extensively studied, revealing the vast potential of these beneficial partnerships in sustainable agriculture. The article discusses various types of beneficial microorganisms, including mycorrhizal fungi, rhizobia, and plant growth-promoting bacteria, elucidating their mechanisms of action and their impact on plant health and productivity. Furthermore, it examines the application of microbial inoculants and biofertilizers in crop production systems, emphasizing their potential to enhance crop yield, nutrient use efficiency, and stress tolerance. The review also explores emerging technologies, such as metagenomics and synthetic biology, in harnessing the full potential of beneficial microorganisms for crop improvement. Overall, we highlight the importance of beneficial microorganisms in sustainable agriculture and provide insights into their promising applications in crop enhancement.

Maize (Zea mays L.) stands as one of the most extensively cultivated crops globally, offering significant contributions to food, animal feed, and biofuel production. Maize yield is significantly impacted by diverse climate and soil variables, yet its productivity faces hindrances due to abiotic stresses, including drought, salinity, extreme temperatures, and cold conditions. In confronting these hurdles, the field of crop breeding has undergone a transformation thanks to high-throughput sequencing technologies (HSTs). These advancements have streamlined the identification of beneficial quantitative trait loci (QTL), associations between markers and traits (MTAs), as well as genes and alleles that contribute to crop improvement. Presently, well-established omics techniques such as genomics, transcriptomics, proteomics, and metabolomics are being amalgamated into maize breeding studies. These approaches have unveiled new biological markers that are being employed to bolster maize's capacity to withstand an array of challenges. In this chapter, we explore the current knowledge regarding the morpho-physiological and molecular mechanisms that underlie the resistance and tolerance of maize to biotic and abiotic stresses. The focus is on utilizing omics techniques to augment maize's ability to withstand these challenges. Moreover, it emphasizes the significant potential of integrating various omics techniques to tackle the challenges presented by biotic and abiotic stress in maize productivity, contrasting with singular approaches.

During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, whereas it is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 300 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops.

Paleogenomics focuses on the recovery, manipulation, and analysis of ancient DNA (aDNA) from historical or long-dead organisms to reconstruct and analyze their genomes. The aDNA is commonly obtained from remains found in paleontological and archaeological sites, conserved in museums and in other archival collections. Herbarium collections represent a great source of phenotypic and genotypic information, and their exploitation allowed to infer and to clarify previously unsolved taxonomic and systematic relationships. Moreover, Herbarium specimens offered a new source to study phenological traits in plants and to disentangle biogeography and evolutionary scenarios of species. More recently, advances in the molecular technologies went in parallel with the decreasing costs of the next-generation sequencing (NGS) approaches, that paved the way to the utilization of aDNA for whole-genome studies. Although many studies have been carried out combining modern analytic techniques and Herbarium specimens, this research field is still relatively unexplored, due to the need of improving strategies for aDNA manipulation and exploitation. The higher susceptibility of aDNA to degradation and contamination during the Herbarium conservation and manipulation, and the occurrence of biochemical post-mortem damages, can result in the more challenging reconstruction of the original DNA sequence. Here, we review the methodological approaches that have been developed for the exploitation of historical Herbarium plant materials, such as the best practices for aDNA extraction, amplification and genotyping. We also focus on some strategies to overcome the main problems related to the utilization of Herbarium specimens for their exploitation in plant evolutionary studies.

The integration of Wireless Sensor Networks (WSNs) into agricultural areas has had a significant impact and has provided new, more complex, efficient, and structured solutions for enhancing crop production. This research reviews the role of Wireless Sensor Networks (WSNs) in monitoring the macro-nutrient content of plants. The review study focuses on identifying the types of sensors used to measure macro-nutrients, determining sensor placement within agricultural areas, implementing wireless technology for sensor communication, and selecting device transmission intervals and ratings. The study of NPK (Nitrogen, Phosphorus, Potassium) monitoring using sensor technology in precision agriculture is of high significance in efforts to improve agricultural productivity and efficiency. In addition to fostering technological innovations and precision farming solutions, in future this research aims to increase agricultural yields, particularly by enabling the cultivation of certain crops in locations different from their original ones.

Fire-related cues could alter vegetation community composition by promoting or excluding different types of plants. Smoke-derived compounds have been a hot topic in plant and crop physiology. There are some fire-prone areas in Australia, both Americas, some Mediterranean countries and regions with extensive prescribed or illegal burning like central European, which are subjected to a direct impact of fire (heat, minerals accumulation from the burning matter) and/or the indirect one (smoke) and undergo compositional and structural changes. This review first updates information about the effect of the compounds on plants' kingdoms and focuses on the research advances in the field of smoke compounds and attempts to gather and summarize the recent state of research and opinions on the role of smoke-derived compounds in plants' lives. We finish our review by discussing major research gaps, which include: Why plants respond to smoke chemicals? Is the response of seeds to smoke and smoke compounds an evolutionary-driven trait allowing plants to adapt to local environment?

Accurate prediction of crop production is essential in effectively managing agricultural countries' food security and economic resilience. This study evaluates the performance of statistical and machine learning-based methods for large-scale crop production forecasting. We predict the quarterly production of 325 crops (including fruits, vegetables, cereals, non-food, and industrial crops) across 83 provinces in the Philippines. Using a comprehensive dataset of 10,949 time series over 13 years, we demonstrate that a global forecasting approach using a state-of-the-art deep learning architecture, the transformer, significantly outperforms traditional local forecasting approaches built on statistical and baseline methods. By leveraging cross-series information, our proposed way is scalable and works well even with time series that are short, sparse, intermittent, or exhibit structural breaks/regime shifts. The results of this study further advance the field of applied forecasting in agricultural production and provide a practical and effective decision-support tool for policymakers that oversee the farm sector on a national scale.

Anthracnose and root rot are major foliar and root diseases of strawberry, respectively that cause most yield and quality losses. Anthracnose is caused by multiple species belonging to Colletotrichum spp complex. This disease alone can causeup to 70% yield loss in North America. Colletotrichum spp. cause several disease symptoms on strawberries, including root, fruit, and crown rot, lesions on petioles and runners, and irregular black spots on the leaf. In many cases, a lower level of infection on foliage remains non-symptomatic (quiescent), posing a challenge to growers as these plants can be a significant source of inoculum for the fruiting field. Reliable detection methods for quiescent infection should play an important role in preventing infected plants' entry into the production system or guiding growers to take appropriate preventative measures to control the disease. The selection of highly effective fungicides and including them in a schedule to prevent the development of fungicide resistance in fungal populations will remain an area of continued research. Disease management methods should entail sensitive molecular testing of suspected fungal isolates for resistance, rotation of products, and monitoring of quiescent infections in transplant materials. Testing and the inclusion of non-chemical methods such as biologicals and biorational treatments especially for soilborne pathogens will also be necessary to make disease management economically feasible and sustainable.

Initial evaluations of the System of Rice Intensification in India and elsewhere have mainly fo-cused on its impact on yield and income and have usually covered just one or two seasons. Re-searchers at the ICAR-Indian Institute of Rice Research conducted a more comprehensive evalua-tion of SRI methods over six years (six wet and six dry seasons), comparing these methods with three other rice crop management systems: modified, partially mechanized SRI (MSRI); direct-seeded rice (DSR); and normal transplanting with inundated fields (NTP). SRI grain yield was found to be about 50% higher than with NTP (6.35 t ha-1 vs 4.27 t ha-1), while the MSRI yield was essentially the same (6.34 t ha-1) and 16% higher than with DSR (5.45 t ha-1). Compared to NTP, SRI methods significantly enhanced soil microbial populations over time, bacteria by 12%, fungi by 8%, and actinomycetes by 20%. Indicators of biological activity in the rhizosphere were also higher, dehydrogenase by 8.5% and FDA enzymes by 16%. Glucosidase activity, an indicator of soil organic matter, was 78% higher. Relative to normal transplanting methods, SRI reduced GHG emissions by 21%, while DSR reduced them by 23% and MSRI by 13%, which indicated pos-itive effects of the alternate and improved methods over normal transplanting. Economic analy-sis showed that both gross and net economic returns to be higher with SRI than with MSRI and the other management systems evaluated. While the six-year study documented many ad-vantages of SRI crop management, the MSRI version evaluated is a promising adaptation that provide similar benefits but with lower labor requirements.

This study investigates the long-term impact of soil tillage systems and crop residue incorporation on agrophysical properties. A long-term field experiment has been conducted since 1999 at the Ex-perimental Station of Vytautas Magnus University Agriculture Academy. According to the latest edition of the International Soil Classification System, the soil in the experimental field was classified as Planosol, with a silty medium loam texture at a depth of 0–20 cm and a silty light loam at a depth of 20–40 cm. This investigation aimed to assess the long-term impact of reduced tillage systems, straw, and green manure combinations on soil physical properties, including soil shear strength, and soil aggregate stability. Studies were carried out on winter wheat crops in 2014, 2017, and 2023. The treatments were arranged using a split-plot design. In a two-factor field experiment, one part of the experimental field had straw removed, while the other part had the entire straw yield chopped and spread at harvest (Factor A). The subplot factor (Factor B) included three different tillage sys-tems: conventional deep ploughing, cover cropping for green manure with no-till, and no-tillage. Soil samples were analysed in the Laboratory of Agrobiology at Vytautas Magnus University Ag-riculture Academy. The findings indicate that long-term application of reduced tillage significantly increases soil shear strength. Shallower tillage depths lead to higher soil shear strength, while the effect of spreading plant residues is relatively lower. Long-term tillage of different intensities, spreading plant residues, and catch crop cultivation for green manure did not significantly affect soil structure. However, soil structural stability was found to be highly dependent on soil tillage. Cover cropping for green manure with no-till and no-tillage positively affected soil aggregate sta-bility in the upper (0–10 cm) and 10–25 cm layers.

In sustainable agriculture, plant nutrients are the most important elements. Biofertilizers introduce microorganisms that improve the soil nutrients and increase their accessibility to crops. In order to meet the demands of a growing population, healthy crops need to be produced using the right type of fertilizers to provide them with all the major nutrients they require. However, an increasing dependency on chemical fertilizers is destroying the environment and negatively af-fecting the health of humans. Thus, using microbes as bioinoculants as the best replacement of chemical fertilizers as eco-friendly way for plant growth and soil fertility is believed to be the best method for improving plant growth and soil fertility. In sustainable agriculture, these microbes provide significant benefits to crops. In addition to colonizing plant systems (epiphytic, endo-phytic, and rhizospheric), beneficial microbes play a key role in absorbing nutrients from surrounding ecosystems. Plant associate microbes can promote plant growth regardless of natural and extreme conditions. Plant growth promoting microbes promote plant growth through a variety of direct and indirect methods, such as nitrogen fixation, plant growth hormone production, siderophores, HCN, several hydrolytic enzymes, and potassium, zinc, and phosphorus solubilization. Research on biofertilizers has been extensive and even available, which demonstrates how these microbes can deliver nutrients to crops in sufficient quantities to enhance their yield. This review examines in detail the direct and indirect mechanisms of PGPR action and their interaction in plant growth and resistance.

To identify soil microbial communities, it is essential to understand their phenotypic and genetic characteristics. Plant growth-promoting rhizobacteria (PGPR) are bacteria capable of promoting plant growth. In addition, rhizobacteria also serve as biological control agents. In addition to fixing atmospheric nitrogen, solubilising insoluble phosphate and secreting hormones such as indoloacetic acid, kinetics and gibberellins, plant growth-promoting rhizobacteria help plants grow directly. Furthermore, they facilitate plant growth indirectly by inducing systemic resistance, siderophores, antibiotics, lytic enzymes, hydrogen cyanide production and stress regulation. This review examines in detail the direct and indirect mechanisms of PGPR action and their interaction in plant growth and resistance.

Sources of resistance to powdery mildew in wild barley (Hordeum vulgare subsp. spontaneum) collected in Jordan, Lebanon, and Libya Jerzy H. Czembor 1, *, Elżbieta Czembor 1 1 Plant Breeding and Acclimatization Institute – National Research Institute (IHAR-PIB), Radzikow, 05-870 Błonie, Poland; e.czembor@ihar.edu.pl (E.C.) *Correspondence: j.h.czembor@ihar.edu.pl) Abstract Barley powdery mildew (BPM) is caused by the pathogen Blumeria graminis f.sp. hordei (Bgh). It is an economically important disease and plant pathologists are looking for new sources of resistance to BPM. Barley genetic resources present in gene banks are often a rich source of disease resistance to be used by breeders. These new sources of resistance to BPM are often used in combination (pyramiding) with those that are already used in modern cultivars. Barley accessions, including the wild subspecies Hordeum vulgare subsp. spontaneum (Hvs), are stored in many gene banks and often are a valuable source of economically important characteristics. This source of biodiversity should be more efficiently used to improve barley in the process of plant breeding. However, their proper characterization and availability are urgently needed. The resistance to BPM in 81 accessions of wild barley (Hvs) collected in Jordan (47), Lebanon (23), and Libya (11) was investigated. The seed samples of these accessions were obtained from the ICARDA gene bank and collected in 10 expeditions from 1981 to 1995. Twenty European differential isolates of BPM were used to select accessions with efficient resistance. Thirty-one resistant single plant lines were selected from 15 accessions from Jordan and Libya based on tests performed with the most avirulent isolate of Bgh available. These resistant single plant lines were tested for the presence of specific resistance genes using a differential set of Bgh isolates. After analysis of obtained results, it was concluded that all tested 31 single plant lines of wild barley have genes for resistance that are not represented in the Pallas isolines differential s. Twenty-six lines of Hvs selected from accessions originated in Jordan and Libya showed resistance reaction to all isolates used. Identified new sources of effective resistance to BPM in single plant lines of Hvs will be further tested and used in barley pre-breeding programs. Keywords: Blumeria graminis; resistance genes; resistance; germplasm; gene bank;

Crop breeding is an important global strategy to meet sustainable food demand. CRISPR-Cas is a most promising gene-editing technology for rapid and precise generation of novel germplasm, and leads to revolutionary crop breeding innovation. In this review, we summarize recent advance of CRISPR/Cas technology in gene function analyses and generation of new germplasms with increased yield, improved product quality, and enhanced resistance to biotic and abiotic stress. We highlight their applications and breakthroughs in agriculture including crop de novo domestication, decoupling the gene pleiotropy tradeoff, crop hybrid seed conventional production, hybrid rice asexual reproduction, and double haploid breeding. Moreover, the challenges and development of CRISPR/Cas technology in crops are also discussed.

Crop yield enhancement is crucial for ensuring food security and meeting the growing global demand for agricultural products. The development of innovative technologies, such as CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats), has provided new avenues for precise genetic modifications in crops. In this research article, we explore the application of CRISPR technology for crop yield improvement. We discuss the methodology, present recent results, and engage in a comprehensive discussion of the potential benefits, challenges, and future prospects of CRISPR-mediated crop yield enhancement.

The creation of crop-type maps from satellite data has proven challenging, often impeded by a lack of accurate in-situ data. This paper aims to demonstrate a method for crop-type (ie. Maize, Wheat and Other) recognition based on Convolutional Neural Networks using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery. Classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for other. Given that wheat and maize are the two most common food crops globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved crop-type monitoring globally. Challenges remain in addressing the noisy aspect of street-level imagery (ie. buildings, hedgerows, automobiles, etc.), where a variety of different objects tend to restrict the view and confound the algorithms

Plant roots host various microorganisms around and inside their roots, known as the root microbiome. To become healthy and productive, plants should keep under surveillance niches around the roots to recognize disease-causing microbes and similarly exploit the services of beneficial microorganisms in nutrient acquisition, stress mitigation and growth promotion. Here we presented the communication strategies between plant roots and root-associated microbes in improving plant growth and yield. Understanding how plant root and root-associated microbes communicate is vital in designing ecofriendly strategies for targeted disease suppression and improved plant growth that will help in sustainable agriculture.

The rye (Secale cereale L.) crop shows a high potential to collaborate in the sustainability of high Andean livestock because it supports the agroclimatic conditions and acid soils in the Peruvian Andes. The production of green forage, hay, and grain of the rye crop in acid soils was studied with the use of different levels of phosphorus and potassium fertilization in four local rye ecotypes (CBI-001, CSM-001, CJS-001, and CCE-001). The green forage yield (FV) ranged from 32.35 to 53.62 t ha-1, dry matter from 6.05 to 8.56 t ha-1, and hay from 7.0 to 10.36 t ha-1; nutritional levels ranged from 9.02 % to 13.56 % protein and 6.50 to 7.75 % ash levels, mainly with differences between ecotypes (p<0.05). No differences existed between fertilization levels for the number of stems per plant, and flowering stems per plant, and grains per ear (p>0.05). Also, CBI-001 and CCE-001 were superior with 1868.4 and 1797.8 kg ha-1 of grain, respectively (p=0.0072); the use of 60 kg ha of Nitrogen, 120 kg ha-1 of P2O5 and 80 kg ha-1 of K2O gave higher grain and residue yields. The high nutritional value and yield of the rye ecotypes studied in acid soil conditions and without irrigation can be an alternative for livestock feeding and grain production in the rainy season in the Andes as a dual-purpose crop.

Plant N concentration (PNC) has been commonly used to guide farmers in assessing maize (Zea mays L.) N status and making in-season N fertilization decisions. However, PNC varies based on the development stage and therefore a relationship between biomass and N concentration is needed (i.e., critical N dilution curves; CNDC) to better understand when plants are N deficient. A few CNDC have been developed and used for plant N status diagnoses, but have not been tested in the US Midwest. The objective of this study was to evaluate under highly diverse soil and weather conditions in the US Midwest the performance of CNDCs developed in France and China for assessing maize N status. Maize N rate response trials were conducted across eight US Midwest states over three years. This analysis utilized plant and soil measurements at V9 and VT development stages and final grain yield. Results showed that the French CNDC (y=34.0x-0.37, where y is critical PNC, x is aboveground biomass) was better with a 91% N status classification accuracy compared to only 62% with the Chinese CNDC (y=36.5x-0.48). The N nutrition index (NNI), which is the quotient of the measured PNC and the calculated critical N concentration (Nc), based on the French CNDC was significantly related to soil nitrate-N content (R2=0.38-0.56). Relative grain yield on average reached a plateau at NNI values of 1.36 at V9 and 1.21 at VT, but for individual sites ranged from 0.80 to 1.41 at V9 and from 0.62 to 1.75 at VT. The NNI threshold values or ranges optimal for crop biomass production may not be optimal for grain yield production. It is concluded that the CNDC developed in France is suitable as a general diagnostic tool for assessing maize N status in US Midwest. However, the threshold values of NNI for diagnosing maize N status and guiding N applications vary significantly across the region, making it challenging for guiding specific on-farm N management. More studies are needed to determine how to effectively use CNDC to make in-season N recommendations in the US Midwest.

In order to ensure food security worldwide in the face of current climatic changes, a higher quality and quantity of crops is necessary to sustain the growing human population. By developing a sustainable circular economy and biorefinery approaches, we can move from a petroleum-based to a bio-based economy. Plant biostimulants have long been considered an important source of plant growth stimulants in agronomy and agro-industries with both macroalgae (seaweeds) and microalgae (microalgae). There has been extensive exploration of macroalgae biostimulants. A lack of research and high production costs have constrained the commercial implementation of microalgal biostimulants, despite their positive impact on crop growth, development and yields. The current knowledge on potential biostimulatory compounds, key sources and their quantitative information from algae is summarized in the present review. Our goal is to provide a brief overview of the potential for microalgal biostimulants to improve crop production and quality. A number of key aspects are discussed, including the biostimulant effects caused by microalgae extracts, as well as the feasibility and potential for cocultures and coapplication with other biostimulants and biofertilizers. This article also discusses the current knowledge, recent developments and achievements in extraction techniques, types of applications, timings of applications. Ultimately, this review highlights the potential for microalgal biostimulants for sustainable agricultural practices, the algal biochemical components contributing to these traits, and finally bottlenecks and involved prospects in commercializing microalgal biostimulants.

In this article, a method for the moisture mapping of the soil surface of agrophytocenosis was proposed using neural network based on synchronized radar and multispectral optoelectronic data of Sentinel-1,2. To verify the developed method, data from two experimental plots were used. These plots were located two on irrigated soybean crops. The first of them was located on the right bank (1st plot) and the second one on the left bank (2nd plot) of the down part of Volga River. Two experimental soil moisture geo-datasets were done by measurements and geo-referencing points using gravimetric method (1st plot) and with proximal sensing method (2nd plot) using Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict reflection coefficient of an electromagnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated on the basis of Sentinel-1 data) to the index (calculated on the basis of data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). Such way calculated index reveals showed a significantly greater dependence on soil moisture than on vegetation height that was been used in previous studies. The retrieved values of soil moisture were compared with the soil moisture measured in-situ. The proposed method with a determination coefficient of 0.44-0.65 and a standard deviation of 2.4%-4.2% for the 1st plot as well as with and of the same metrics for the 2nd allows predicting the soil moisture of both a test plots covered by soybean plants, relative to soil moisture measured in-situ. The conducted research created the scientific basis for a new technology for remote sensing the moisture content of the soil surface of agrophytocenosis as an element of the precision farming system and agroecology.

Climate change is a large and challenging collective action problem facing the world today. This seminar review impacts of climate change on r yield of major cereal crops. Literatures have indicated that climate change would likely have positive impact in the highland agro-ecology in the short run. However, in the long run climate change will have negative impact in all agro-ecology. Negative impacts will be high in low land agro-ecology. Failure to prepare for climate variability can seriously affect the livelihood of smallholder farmers. Ethiopia by enacting Climate-Resilient Green Economy strategy and National Adaptation Plan aims to minimize the impacts of CC. Despite the existing adaptation and mitigation strategies negative impacts of climate change on agriculture especially on cereal crop production is continued. Thus, both private and public investment on adaptation strategies should be expanded more. Studies regarding the impacts of climate change on labour productivity, labour market, its distributional (which section society mostly by climate change?) and why the existing adaptation strategies are not successful? are not well studied, as most literature are focused on impact of climate change on yield, its adaptation and mitigation strategies.

The concept of omics-based technological approaches has promoted translational research by integrating knowledge from diverse areas to understand their dynamics by exploring the molecular mechanisms underlying various processes paving the way for further improvements in the crops quality by providing sound knowledge on controlling plant diseases. This area is not profoundly investigated a decade back and hence this knowledge has become exceedingly important in modern times as it has got potential for being utilized in crop improvement programs. In this review, the contributions of different omics technologies including genomics, transcriptomics, proteomics, and metabolomics in understanding and materializing the ways involved in the plant pathogen interaction (PPI) is discussed. Furthermore, opportunities, challenges, and perspectives of omics linked to signaling mechanisms have also been highlighted that are significantly linked to plant-microbe interactions.

The rising global population amidst the growing concerns of climate change will have a dire consequence on global food security and socio-economic activities. Wheat is one of the most important staple foods consumed by more than four billion people in the world, but climate change impacts account for a decline of 5.5% in wheat yield and predictions indicate that the production could further dwindle by nearly 30% in 2050, due to trends in temperature, precipitation, and carbon dioxide. An effective annual crop estimate is necessary not only to inform government the status of national food security, but also is used to determine the benchmark on which agricultural commodities are priced in the market. Thus, annual crop monitoring and yield estimate is paramount to determine the amount of wheat imports required to make up for the shortfalls in the national wheat production in South Africa, which has been a net importer of wheat since 1998. A joint project between South Africa and Poland investigated satellite based-crop growth monitoring using Sentinel 2 and determined the most distinguishable crop phenology for an accurate winter wheat classification during the growing season from August – December with Random Forest (RF) algorithm. The winter wheat crop was more accurately identified during the crop ‘heading’ stage in October yielding the highest user’s (75.56%) and producer’s (92.52%) accuracies, despite the relatively lower overall accuracy (78.14%) compared to that of December with OA of 83.58% obtained during the maturity stage. This study, therefore, confirms the suitability of sentinel 2 for an effective phenology-based winter wheat crop classification during the heading stage, reducing the ambiguity of spectral confusion created with surrounding grass and maize crops.

Agriculture has been experiencing a difficult situation because of limiting factors in its production, Natural biostimulants (NBs) have emerged as a novel alternative. This study reviews NBs produced through Solid-State Fermentation (SSF) from organic waste, focusing on the processes and production methods. The aim is to highlight their potential for improving agricultural productivity and promoting sustainable agriculture. Through a literature review, the effects of NBS on crops were examined, as well as the challenges associated with their production and application. The importance of standardizing production processes, optimizing fermentation conditions, and assessing their effects on different crops is emphasized. Furthermore, future research areas are identified, such as enhancing production efficiency and evaluating the effectiveness of SSF-produced NBS in different agricultural systems. In conclusion, SSF-produced NBS offers a promising alternative for sustainable agriculture, but further research and development are needed to maximize its efficacy and enable large-scale implementation.

Crop disease classification has always been a critical and persistent problem in the field of agricultural and forestry sciences, where often we do not have access to a sufficient number of samples to know the distribution of real-world samples. How to make full use of the existing data is the starting point of our thinking. To address this problem, this paper proposes a supervised image augmentation method Negative Contrast, which uses the contrast images of existing disease samples after removing disease areas as negative samples for image augmentation when samples are relatively scarce. Numerous experiments have shown that several classical models using this augmentation method have improved in disease classification of four crops, rice, wheat, corn, and soybean, with a maximum accuracy improvement of 30.8%. In addition, the comparative analysis of attentional heat map shows that the model using Negative Contrast is more accurate and intense on the area of interest of diseases, and thus reflects better generalization ability in real-world disease classification. Our dataset and codes can be found in https://www.kaggle.com/datasets/w970704112/corn-wheat-rice-soybean and https://github.com/hiter0/contrastaug .

Purpureocillium lilacinum PL11 (formerly Paecilomyces lilacinus) is a ubiquitous fungus found in several habitats, mainly in the soil, and belongs to the Ophiocordycipitaceae family. This fungus was shown to have potential applications in agriculture as a biocontrol agent and biofertilizer. P. lilacinum can inhibit plant‒pathogenic fungi and nematodes, including root-knot and potato cyst nematodes. Additionally, P. lilacinum produces siderophores and indole-3-acetic acid (IAA), enhancing plant development and increasing soil nutrient availability. P. lilacinum has been extensively studied in various commodities, including pineapple, maize, soybean, and legume. Most studies have evaluated P. lilacinum as a biocontrol agent, especially for nematode control. Other studies have evaluated the production of secondary metabolites and bioremediation, and few studies have used this fungus as a plant growth promoter. This review addressed important aspects of using this fungus. P. lilacinum is a promising fungus that can be used for agricultural production, reducing environmental impact, and thus collaborating for a sustainable agriculture production system.

The Philippines is an archipelagic country composed of different indigenous cultural communities (ICCs) spread across various islands. Many of these Indigenous Peoples (IP) are marginalized and do not have access to common resources enjoyed in the lowland areas. An initial assessment focusing on the evaluation of their available livelihood and resources was conducted, in order to find common solutions to existing problems. The assessment took place at the office of the tribal council of Matigsalog in barangay Datu Salumay, Marilog District, Davao City from April 11 to 13, 2023. There were about 42 participants in total for Matigsalog tribes (26 men and 16 women) who attended the meeting and there were 15 participants from the Ata tribe (7 men and 8 women). Validation of the study separately took place through a brief presentation of the results before 15 members of the tribe and a hiking visit in their farmlands. The study determined the existing livelihoods in the two areas which are mainly agricultural, farming of crops (rice, corn, cassava, sweet potato) and fruit trees (coconut, banana, durian, jackfruit, pomelo) and the resources as well as the community’s knowledge, systems, and practices with regards to agricultural crops including climate change. Their sustainable farming practices include intercropping, seed-saving, and exchange practices, preserving these crop varieties and ensuring their availability for future planting seasons, no usage of chemical fertilizers and pesticides. To conclude, the tribe’s aspirations and resulting recommendations are summarized, to facilitate more directed and effective governmental assistance.

This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production and reducing waste through informed decisions about planting, watering, and harvesting crops. The paper discusses the current state of machine learning and IoT in agriculture, highlighting key challenges and opportunities. It also presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, farmers can make more informed verdicts about factors that affect crop growth. Eventually, the integration of these technologies can transform modern agriculture by increasing crop yields while minimizing waste. In our studies, we achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99. 46% using Naïve Bayes Classifier, and Hoeffding Tree algorithms. Our results indicate that we achieved high accuracy results in our experiments in order to increase crop growth.

In recent years, the interest in increasingly sustainable agriculture has also turned attention towards new crops which could enter the food chain as new protein and oil sources or that can be used as new cover crops. In this scenario, Camelina sativa is a perfect crop to study. Camelina is an annual herbaceous plant belonging to the Brassicaceae which is interesting for its oil content, since the seeds contain about 40% of oils, with a high level of polyunsaturated fatty acids (30-40% alpha linolenic acid, 15-25 % linoleic acid, 15% oleic acid and about 15% eicosenoic acid). It is a hexaploid species (2n = 40, genome size ~782 Mb) characterized by rapid growth, short life cycle (85-100 days for spring varieties, 190-210 for autumn varieties) and low input cultivation needs. All these traits make it suitable for use in marginal areas. However, its use in feed and food is limited by the presence of glucosinolates (GLS). GLS are sulfur molecules involved in plant defense. In recent years, they have been studied not only as anti-nutritionals, but also for their anti-carcinogenic effects against chronic inflammatory and heart diseases and for their use as natural pesticides. Given the recent interest in camelina and its highly nutritious oil, eight pure lines and a synthetic population were compared in two different growing periods, spring and winter. In this work, the genetic materials were characterized for different phenotypic traits, yields and yield components, bromatological and glucosinolate content. The results confirmed that in North Italy camelina has higher yields if cultivated in the autumn-winter period (about 2 t/ha vs 0.6 t/ha), furthermore, a negative correlation was found between spring and winter yields, indicating that varieties that produce more in winter cultivation produce less in spring cultivation. Moreover, to our knowledge, it is the first work in which a synthetic population of Camelina sativa has been tested and proved to be a valid solution for use in various environments both for its adaptability and for the low content of glucosinolates (about 17 mmol/ kg).

The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on the onion crop and the challenges presented throughout its phenological cycle. Aerial monitoring using unmanned aerial vehicles (UAV) and digital image processing were used to identify patterns in the onion crop, including humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 ton/ha, but only 25.00 ton/ha was reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being of medium size. It is estimated that approximately 20% of the production was lost due to blight and unfavorable weather conditions.

Heavy metals and metalloids (HMs) are environmental pollutants, most notably cadmium, lead, arsenic, mercury, and chromium. When HMs accumulate to toxic levels in agricultural soils, these non-biodegradable elements adversely affect crop health and productivity. The toxicity of HMs on crops depends upon factors including crop type, growth condition, and developmental stage; nature of toxicity of the specific elements involved; soil physical and chemical properties; occurrence and bioavailability of HM ions in the soil solution; and soil rhizosphere chemistry. HMs can disrupt normal structure and function of cellular components and impede various metabolic and developmental processes. This review evaluates: (1) HM contamination in arable lands through agricultural practices, particularly due to chemical fertilizers, pesticides, livestock manures and compost, sewage-sludge based biosolids, and irrigation; (2) factors affecting the bioavailability of HM elements in the soil solution, and their absorption, translocation, and bioaccumulation in crop plants; (3) mechanisms by which HM elements directly interfere with the physiological, biochemical, and molecular processes in plants with particular emphasis on the generation of oxidative stress, inhibition of photosynthetic phosphorylation, enzyme/protein inactivation, genetic modifications, and hormonal deregulation, and indirectly through inhibition of soil microbial growth, proliferation, and diversity; and (4) visual symptoms of highly toxic non-essential HM elements in plants with an emphasis on crop plants. Finally, suggestions and recommendations are made to minimize crop losses from suspected HM contamination in agricultural soils.

Soil-borne oomycetes include devastating plant pathogens that cause substantial losses in the agricultural sector. To better manage this important group of pathogens, it is critical to understand how they respond to common agricultural practices, such as tillage and crop rotation. Here, a long-term field experiment was established using a split-plot design with tillage as the main plot factor (conventional tillage [CT] vs. no till [NT], 2 levels) and rotation as the subplot factor (monocultures of soybean, corn, or wheat, and corn-soybean-wheat rotation, 4 levels). Post-harvest soil oomycete communities were characterized over three consecutive years (2016-2018) by metabarcoding the Internal Transcribed Spacer 1 (ITS1) region. The community contained 292 Amplicon Sequence Variants (ASVs) and was dominated by Globisporangium spp. (85.1% in abundance, 203 ASV) and Pythium spp. (10.4%, 51 ASV). NT decreased diversity and community compositional structure heterogeneity, while crop rotation only affected the community structure under CT. The interaction effects of tillage and rotation on most oomycetes species accentuated the complexity of managing these pathogens. Soil and crop health represented by soybean seedling vitality was lowest in soils under CT cultivating soybean or corn, while grain yield of the three crops responded differently to tillage and crop rotation regimes.

China is a large agricultural country that produces a large amount of crop straw every year. Thus, the development of cost-effective and economic application of invasive plants is warranted. Biochars derived from crop straw has been proven to be promising for adsorbent materials. However, less studies have focused on biochar derived from different types of crop straw as adsorbent under the same conditions to compare their adsorption performance. Here, we characterized the five biochars in the same system (600 ℃). In results, GBC has higher ash content, pH, CEC, specific surface area, mineral composition and oxygen-containing functional groups. The adsorption kinetics can be explained adequately by pseudo-second-order model and Langmuir model, indicating that the adsorption behavior of the biochar is both physical adsorption and chemical adsorption, the adsorption process includes complexation reaction, cationic π bond, ion precipitation and electrostatic adsorption. In conclusion, GBC exhibited higher metal equilibrium adsorption capacities (125 mg·g-1 for Pb2+, 29 mg·g-1 for Cd2+). The solution pH, biochar dosing, pyrolysis temperature and the properties of these heavy metals were responsible for adsorption capacity, thus showing stronger affinity and better adsorption effect. Our results are important for the selection and utilization of plant-based biochar for different heavy metals.

Early-season crop-type data are required for a variety of agricultural monitoring and decision-making applications. The early season in this study referred to the prophase and middle of a growth season. Early-season detection of crop types remains challenging because of limited discriminative features, especially for landscapes that are characterized by complex cropping patterns. In fact, different remote-sensing satellites can increase the frequency of data acquisition, which can provide more information in the early season. Moreover, optical and radar sensors have different degrees of sensitivity to crop parameters. Therefore, the integration and application of two types of multitemporal data are of great significance to improve the accuracy and timeliness of crop type detection.In deep learning (DL), convolutional neural network (CNN) and recurrent neural network (RNN) models have great potential for temporal feature extraction. Compared with RNNs, CNNs usually have fewer parameters and are more conducive to determining early-season detection dates of different crop types, which requires a lot of training because of the need to model on different dates. Nevertheless, revisit dates and temporal intervals of different satellites are usually different, resulting in different data acquisition time series; these data cannot be directly used as input for the same convolutional layer. To address this challenge, a Dual-1DCNN was bulit based on the CNN model in this study. Moreover, an incremental training method was used to attain the network on each data acquisition date and obtained the best detection date for each crop type in the early season. A case study for Hengshui City in China was conducted using time series of Sentinel-1A (S1A) and Sentinel-2 (S2). To verify this method, classical methods support vector machine (SVM) and random forest (RF) were implemented. The results demonstrated the following: (1) the Dual-1DCNN extracted discriminative features from S1A and S2 time series at the early season by producing the highest overall accuracy (OA: 87.23%); (2) for summer maize, cotton, and common yam rhizome, the Dual-1DCNN achieved F1 values of 92.39%, 87.71%, and 84.38%, respectively, at the early season (moreover, the early seasons were almost 40, 70, and 80 days before the end of the growth seasons, respectively). These findings suggested that the Dual-1DCNN is promising for the accurate and timely detection of crop types.

This study assessed the literature about the use of high tunnels (HTs) in agricultural systems in the United States since the 2009 launch of the Natural Resources Conservation Service (NRCS) High Tunnel Initiative. This NRCS program led to an increase in HT adoption nationwide. The literature searches were conducted using the Web of Science (WoS) database. The final sample was 133 peer-reviewed articles published between 2009 and February 2023. We used CiteSpace 6.2.R1 and Gephi 0.9.2 to conduct co-citation, co-author, co-institution, and clustering techniques. The findings showed that the peer-reviewed literature about HT use has increased since 2009, substantially rising between 2017 and 2021. Horticulture was the top subject category in the literature, and most articles were published in peer-reviewed journals of the American Society for Horticultural Science (i.e., HortTechnology and HortScience). The research field evolved from general HT practices, nutrient management, and plant pathology to focus on trials of specific crops and integrated pest management. The institutions with the most contributions to the HT literature were Kansas State University, the University of Florida, Michigan State University, Purdue University, and the University of Minnesota. The patterns of HT research revealed in this study offer a greater understanding of the current state of knowledge to inform the focus of future research.

The characteristics of the Sentinel-2 mission with a decametric resolution and frequent acquisitions allow to improve the identification of crops. The majority of the studies on crop classification using RS were targeted at herbaceous and gramineous crop classes while fewer results were obtained on woody crops which present a strong variability in management practices that make their identification difficult. Thus, this study aimed to propose a rapid, accurate, and cost-effective analytical approach for the delineation of fruit orchards (OC), vineyards (VY), and olive groves (OL) in the Mediterranean (Southern France) considering two locations. A classification based on phenology metrics (PM) de-rived from temporal Sentinel-2 time series was developed to perform the classification. The PM were computed by fitting a double logistic model on temporal profiles of vegeta-tion indices to delineate OC, VY, and a DC class gathering all remaining surfaces. The generated PM were introduced in a random forest (RF) algorithm to identify woody crops across the two sites. The method was tested on different vegetation indices, the best results being obtained with the leaf area index (LAI). To delineate OL in the DC class, the tem-poral features of the green chlorophyll vegetation index (GCVI) were found to be the most appropriated with a typical drop of the signal during the mid-season (DOY 150-250). As a final result, we obtained an overall accuracy ranging from 89-96% and Kappa of 0.86-0.95 by considering each study site and year (2016-2021), separately. This accuracy is much better than applying the RF algorithm on the LAI times series, which led to a Kappa rang-ing between 0.3 and 0.52 and demonstrates the interest of using phenological traits rather than the raw time series of the RS data. The method can be well reproduced from one year to another. Moreover, it is possible to apply the classification model of a given year to an-other, keeping good accuracy. This is an interesting feature to reduce the burden of col-lecting ground truth information. On the contrary, the use of a classification model cali-brated in one site and applied to another led to a strong degradation of the classification accuracy. Woody crop phenology is dependent on site climatic conditions as well as the cultivar and management practices that can differ from one site to another.

This study aimed to investigate the possibility of using one-shot hyperspectral airborne images to recognize crops for an area with many small plots. The results showed that unsupervised clustering methods could classify crops with an accuracy of 80%, which improved to 90% when restricted to only grain crops, using a single airborne hyperspectral recording. However, additional layers such as NDVI, DTM, slope, and aspect did not improve classification accuracy. For comparison, the accuracy of clustering time series Sentinel-2 images with NDVI layers and DTM-derived data yielded an accuracy of: 74% ,Sentinel-2 time series 68% and single one registration before harvest - 39%. The results of the random forest classification were slightly less accurate due to a lack of sufficient reference data. However, it is challenging to verify the reported accuracy of crop recognition in the literature above 90% due to differences in analysis methodologies, reference data selection, pixel/object approaches, metric choice, and calculation formulas used.

Lupinus mutabilis Sweet (Fabaceae), “tarwi” or “chocho”, is an important grain legume in the Andean region. In Peru, studies on tarwi have been mainly focused on morphological features, however, the have not been molecularly characterized. Currently, it is possible to explore genetic parameters of plants with reliable and modern methods like genotyping-by-sequencing (GBS). We here for the first time used single nucleotide polymorphisms (SNPs) markers to infer the genetic diversity and population structure of 89 accessions of tarwi from nine Andean regions of Peru. A total of 5922 SNPs distributed along all chromosomes of tarwi were identified. STRUCTURE analysis revealed that this crop is grouped into two clusters. A dendrogram was generated using the UPGMA clustering algorithm and, similar to the principal coordinate analysis (PCoA), it showed two groups that correspond to the geographic origin of the tarwi samples. AMOVA showed a reduced variation between clusters (7.59 %) and indicated that variability within populations is 92.41 %. Population divergence (Fst) between clusters 1 and 2 revealed low genetic difference (0.019). We also detected a negative Fis for both populations, demonstrating that, similar to other Lupinus species, tarwi also depends on cross-pollination. SNPs markers were powerful and effective for the genotyping process in this germplasm. We hope that this information is the beginning of the path towards a modern genetic improvement and conservation strategies of this important Andean legume.

To meet an ever global population's food demand, crop yields must be sustained and increased. Drought, which is getting harsher as a result of global warming, is largely impeding the agricultural productivity. Maize is widely used as food and animal feed in many regions of the world, but its yields are largely effected by drought and heat stress. Historical data on climate change predicts that drought and heat stress becoming major threat for maize cultivation in coming years, which will have huge impact on food security of the world especially in Africa and Asia. Thus there is an immense necessary to develop drought tolerant and climate resilient maize to feed the predicted population of the world. Availability and accessibility of crop genetic resources plays a huge role in development of drought-tolerant maize cultivars. A huge genetic resources of maize, including its landraces and crop wild relatives (CWR) have been reported naturally and many of them have stored in National and International gene banks globally. Conventional breeding methods have been tremendously increased maize yields, but these methods frequently fall short of achieving the demand for improved drought stress resistance. In this article, we have briefly discussed about impact of climate variability on crop production, maize yield losses due to drought, drought tolerance in maize landraces and CWR, and origin and evolution of Mexican landraces. This information may help in utilization of these potential resources in various pre-breeding programs.

Precision agriculture and open-source data repositories provide a plethora of field-specific ecological data about agroecosystems, but few mechanisms have been developed to turn that information into management recommendations for crop production. The On-Farm Precision Experiments (OFPE) framework is an agroecological model-based methodology to improve crop manager’s abilities to make field-scale agronomic input decisions. This work evaluates the use of field-specific experiments that employ open-source data and the data emanating from precision agriculture technologies to gain local knowledge of the spatial and temporal variability in agroeconomic performance at the sub-field scale. Quantification of the temporal variability in crop response to inputs (e.g., crop seeding rates, crop rotations, fertilizers, other soil amendments, pesticides, etc.) allows for estimation of the probability that a future management scenario will outcompete another, in terms of crop yield, crop quality, farmer net return, or environmental quality. The challenge is to integrate OFPE into applied management with minimal disruption of stakeholder practices while drawing on historic knowledge about the field and economic constraints. OFPE is the basis of a decision support system that includes a six-step cyclical process that harnesses precision agriculture technology to apply experiments and gather field-specific data, incorporates modern data management and analytical approaches, and generates management recommendations as probabilities of outcomes. The OFPE framework allows field managers to assess the tradeoffs in agronomic input management between the maximization of crop production, quality and profits from production while considering environmental effects.

The rice-wheat rotation is the dominant cropping system in Bihar, where food security of the rural population depends heavily on the production of rice and wheat. In Bihar, climatic shocks induced by low temperatures and terminal heat stress can significantly affect rice and wheat yields. The present work evaluates the benefit of using the monsoon onset as the date for planting rice in reducing thermal stress on rice-wheat systems. High-resolution gridded crop simulations using the APSIM model were performed to simulate potential yields of rice and wheat using the monsoon onset and the farmers’ practice as planting dates. The monsoon onset was calculated using an agronomic definition and farmers’ practice dates were estimated using satellite data. Model outputs were analyzed in terms of planting dates, yields, and the incidence of low temperature stress on rice and high temperature stress on wheat by means of the APSIM yields limiting factors. The results show that the rice planting and harvest dates using the monsoon onset are in general 20-30 days earlier, decreasing the incidence of thermal stress in rice and wheat, and generating higher and more stable yields. These results can help design mitigation strategies for the impacts of climate shocks induced by low and high temperature events in the context of the advances in sub-seasonal and seasonal forecasting, targeting climate services for farmers in Bihar.

About a decade ago, active optical crop canopy sensors are being used to manage in-season variable nitrogen (N) fertilization in cornfields to match the plant demand that occurs mid season, increasing the efficiency compared to broadcast N applications. There were also initiatives of using ultrasonic sensors to measure plant height on-the-go for N application and crop water demand estimation, but no studies have integrated the optical, ultrasonic and canopy temperature for crop water stress assessment. The objective of this chapter is to evaluate the crop water status using infrared thermometry integrated with optical and ultrasonic sensors. Specifics objectives are: (i) evaluate the corn canopy temperature under different previous crop, N rates and irrigation levels; (ii) test a procedure for water stress assessment in commercial cornfields using the integration of sensors, (iii) correlate plant based sensor measurements (N status, plant height and canopy temperature) with grain yield, soil attributes and detailed topographical features, and (iv) study the spatial dependence of canopy temperature. This study was conducted in one small plot study area and on three producer’s fields in 2010. The small plot experiment consisted of two irrigation levels (70 and 100% of evapotranspiration – ET), two previous crop schemes (corn after corn – CC and corn after soybeans – CS), and four N rates (0, 75, 150, 225 kg N ha^-1). Canopy temperature, optical reflectance and plant height was measured from R2 until R6 in the small plots. At the producer’s fields, three long strips across center pivots were used to have a non-limited N and water crop and then continuous georeferenced sensors measurements were taken during side-dress (V11 growth stage) in about 10 hectares in each field. In the small plot study the crop canopy temperature was influenced by the irrigation levels and N rates. The procedure proposed could be used to identify zones in the producer’s field where water stress can be a yield limiting factor other than N derived. Inside the zones considered that water stress played a major whole, there were low correlations between plant height, plant N status and canopy temperature, indicating that the canopy temperature had more influence from water stress than vegetation cover. Concave and lower elevation areas had higher yields compared to convex and high elevation, showing that the detailed elevation mapping can be beneficial to delineate stables zones that possibly could be used in variable irrigation systems. The spatial dependence of canopy temperature was over 65 meters across producers’ sites, showing that the commercial high clearance applicator’s swath width was adequate to obtain accurate maps. The integration of plant N status, plant height and canopy temperature was beneficial to detect water stressed zones in the field. Opportunities can be foresee also for on-the-go N fertilization using integration of these sensors because is likely that water stress can be confounded with different N supply during the growing season and in different zones in the field.

Bacillus thuringiensis (Bt) is a spore-forming bacterium that produces insecticidal proteins and other virulence factors and is considered one of the most successful bioinsecticides available to control pests in agriculture. Bt strains have been reported as endophyte or rhizospheric bacteria, but little is known about the implications of this property of Bt in crop protection. Here, we review if Bt can establish as an endophyte/rhizobacterium and evaluate if Bt as an endophyte/rhizobacterium can simultaneously act against different phytopathogens (fungi, bacteria, insects and viruses) plus promote plant growth. The implications of the proposed review will broaden our understanding of Bt as a versatile entomopathogen by exhibiting differential behavior depending on context.

Intercropping as a practice is very crucial in livelihood sustainability among smallholder farm-ing communities in many growing countries. However, for most cropping systems, the benefits of intercropping have yet to be optimized due to a lack of knowledge regarding spatial ar-rangements and planting densities. The objective of the study was to find out the profitability of maize-vegetable intercropping and the yields obtained from various spatial arrangements and planting densities of intercrops. From May to October 2019 and 2020 respectively, Experimental trials were set up in the rainy season in Northern, Upper West and Upper East regions of Ghana comprising of eight treatments each. A randomized complete block experimental design was used for the field layout with three to four replications. Data was collected on grain and fruit yield and land equivalent ratios was estimated (LER). At the end of the trial, important spatial arrangements and planting densities were identified that can be adopted by smallholder famers for system intensification. For okra, the optimal intercropping system under sufficient rainfalls was 2 okra rows at higher density and 2 maize rows at lower density for Upper West Region. In Upper East Region, the optimal spatial arrangement to recommend is 1 row of maize at recom-mended density: 2 rows of okra at lower density under well distributed rainfalls. For roselle, intercropping with spatial arrangement of 2 rows of maize at higher density: 1 row of okra at recommended density was recommended in Northern Region.

Abiotic stresses, including drought, salinity, cold, heat, and heavy metals, extensively reduce global agricultural production. Approaches such as conventional breeding and transgenic breeding have been widely used to cope with these environmental stresses. The clustered regularly interspaced short palindromic repeat- Cas (CRISPR/Cas) based gene-editing tool has revolutionized due to its simplicity, accessibility, adaptability, flexibility, and wide applicability. This system has a great potential to build up crop varieties with enhanced tolerance against abiotic stresses. In this review, we summarize the most recent findings on understanding the mechanism of abiotic stress response in plants and the application of CRISPR/Cas mediated gene-editing system towards enhanced tolerance to drought, salinity, cold, heat, and heavy metals stresses. Furthermore, in this review, we highlighted the recent advancements in prime editing and base editing tools for crop improvement.

Over the last few years, Earth Observation (EO) data has shifted towards increased use to produce official statistics, particularly in the agriculture sector. National statistics offices worldwide, including in Asia and the Pacific, are expanding their use of EO data to produce agricultural statistics such as crop classification, yield estimation, irrigation mapping, and crop loss estimation. The advances in image classification, such as pixel-based and phenology-based classifications, and machine learning create new opportunities for researchers to analyze EO data applied to agriculture statistics. However, it requires the ground truth (GT) data because classification result mainly depends on the quality of GT. Therefore, in this study, we introduced a random sampling approach to design and collect GT data using EO imagery and ancillary data. As a result of data collection, GT data improve the algorithms and validates classification results. Nevertheless, despite the importance of GT data, they are rarely disseminated as a data product in themselves. Thus, this results in an untapped opportunity to share GT data as a global public good, and improved use of survey and census data as a source of GT data.

Climate change poses a serious threat to global agricultural activity and food production. To address this issue, plant genome editing technologies have been developed to provide an alternative solution for crop improvement. Unlike conventional breeding techniques (e.g., selective breeding and mutation breeding), modern genome editing tools offer more targeted and specific alterations of the plant genome to produce crops with desired traits, such as higher yield and/or stronger resilience to the changing environment. In this review, we discuss the current development and future applications of genome editing technologies in mitigating the impacts of biotic and abiotic stresses on agriculture. We focus specifically on the CRISPR/Cas system, which has been the center of attention in the last few years as a revolutionary genome-editing tool in various species. We also conducted a bibliographic analysis on CRISPR-related papers published from 2012 to 2021 (10 years) to identify trends and possible gaps in the CRISPR/Cas-related plant research. In addition, this review article outlines the current shortcomings and challenges of employing genome editing technologies in agriculture with notes on future prospective. We believe combining conventional and more innovative technologies in agriculture would be the key to optimizing crop improvement beyond the limitations of traditional agricultural practices.

The specialisation and intensification in agriculture have increased the productivity but have also led to the spread of monocultural systems, simplifying production and reducing genetic diversity. The purpose of this study was to propose crop diversification as a tool to increase biodiversity and achieve sustainable and resilient intensive agriculture, particularly in areas with water scarcity. In this paper, a combined Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) was applied to evaluate the environmental and economic sustainability of a differentiated system of cultivation (pomegranate, almond and olive), according to modern intensive and superintensive cropping systems. Based upon the results obtained, it is deduced that pomegranate cultivation generated the highest environmental load, followed by almond and olive. From the financial analysis, it emerged that almond is the most profitable, followed by pomegranate and olive.