Cameroon territory is experiencing significant land use and land cover (LULC) changes since its independence in 1960. But the main relevant impacts are recorded since 1990 due to intensification of agricultural activities and urbanization. LULC effects and dynamics vary from one region to another according to the type of vegetation cover and activities. Using remote sensing, GIS and subsidiary data, this paper attempted to model the land use and land cover (LULC) change in the Centre Region of Cameroon that host Yaoundé metropolis. The rapid expansion of the city of Yaoundé drives to the land conversion with farmland intensification and forest depletion accelerating the rate at which land use and land cover (LULC) transformations take place. This study aims at assessing the impacts of both agriculture and urbanization on the LULC change in the Centre Region of Cameroon. A detailed LULC map from MAPBOX high resolution images and three LULC maps were produced from Landsat TM-OLI images (1984-2015). A maximum likelihood classification techniques using ERDAS Imagine, showed forest decline with a total loss of 54% in thirty years. Also, Landsat and MAPBOX images to which we added 1951 aerial photograph and SPOT 6 (2006) were used to analyse urban growth in the city of Yaoundé. The results show a remarkable urban spatial spread of the metropolis between 1951 and 2015, with a peak in 2000. Images processing enabled us to analyse the long term dynamics of LULC change since the 1950s in this Region using ArcGIS & QGIS software’s. Based on this dynamic, a LULC projection map was produced using Markov model on IDRISI Selva, demonstrating the decrease of the dense forest (45% in 2015 to 0.25% in 2050). It was estimated that by 2050, the entire dense forest can be depleted if nothing is done, while only 12.67% of the secondary forest would remain in the Region. Such a projected map is very useful to decision makers for council development and urban planning. This effective forest depletion ties with the hypothesis that urbanization of Yaoundé and its secondary surrounding satellite cities (within a radius of 30-100km) is a veritable driving force of deforestation.

In Bangladesh, climate change is a major concern because of its geophysical location and climate dependent agriculture. As sessile organisms, crops plants have to face difficulties often in this environmentally vulnerable country. Therefore, this study examines the seasonal trend of two climatic parameters viz. temperature (maximum and minimum) and rainfall over a period of 1983 to 2013. Besides, this study provides insight into the relationship between climatic parameters and crop yield of two major crops viz. rice and wheat during 1997-2013. To assess the relationship of climatic parameters with time and yield using Pearson correlation analysis, time series data used at an aggregate level. SPSS software utilized for this analysis. The cropping seasons such as rice growing seasons Aus (summer rice), Aman (autumn rice) and Boro (winter rice) exhibited a significant increase in maximum and minimum temperature. Rainfall found to have a decreasing trend for all the seasons. This study also revealed that the climatic parameters had significant effects on rice yield, but these results varied among three rice crops. Maximum temperature had positive effects on all rice yields, especially on Aus and Aman. Minimum temperature had a negative effect on Aman rice yield but a positive effect on Aus rice yield. Wheat yield negatively associated with temperature. Rainfall exhibited negative relation with both rice and wheat yield.

Agricultural product like cassava produces huge amounts of waste when processed to consumable goods. The waste generated is generally considered to contribute largely to environmental pollution. This study therefore investigates the waste management practice that is adopted by cassava processors in Ogun State, Nigeria. Five (5) Local Government Areas (LGAs) dominant in processing cassava were selected for the study on the basis of spatial location distribution; landmass and population. The survey involved the use of structured questionnaires administered to cassava processors of the selected LGAs. The Statistical Package for Social Sciences (SPSS) software application and descriptive statistics were used for data analysis. Results of the analysis show that majority (70%) of the cassava processors are females. Cassava peel constitutes 10% of the waste produced, of which 91% are heaped at refuse dump in most communities. Results also reveal that 86.3% of cassava residues are used for animal feeds. Other findings show that the peels when dried are used as bio-fuel for cooking and there is a significant potential for biogas production. From the data captured from respondent during the study, most processors are willing to pay for an improved waste management system. The study therefore suggests proper waste management of cassava waste to minimize environmental pollution.

Aflatoxin contaminated maize is of public health concern in Kenya. Training farmers on good agricultural practice (GAP) has been touted as a mitigative measure. Little is known of the effect of such training on aflatoxin levels in maize grown in Kenya. This study evaluated what effect training farmers on GAP has on aflatoxin levels in maize grown in in maize grown in Kaptumo, Kilibwoni, and Kipkaren divisions in Nandi County. Ninety farmers were recruited for the study and interviewed on GAP. Maize samples were additionally collected from the participating farmers and analyzed for aflatoxins using competitive enzyme-linked immunosorbent assay (c-ELISA). All farmers prepared the land before planting, did correct spacing between the planted crops, carried out weeding, cleaned their stores before use, checked the condition of the maize after harvesting, sorted maize after shelling, and knew aflatoxins. A majority of the farmers (90%) used fertilizers, dried maize after harvesting, knew that aflatoxins were harmful to humans, and used clean transport in transporting the harvested maize. About 98% of farmers did stooking after harvesting and 97% used wooden pallets in the maize stores. The percentage of farmers who practiced early planting, top dressing, crop rotation, raising stores above the ground, applying insecticide after shelling and feeding damaged/rotten seeds to their animals was 84–96%, 62–80%, 67–85%, 86–98%, 63–81%, and 7–21% respectively. About 18/90 (20%) of all farmers reported that they had a relative who had died from liver cancer and the mean aflatoxin levels in season 1 were significantly different from season 2 (1.92 ± 1.07 ppb; 1.30 ± 1.50 ppb). Our findings suggest that although training farmers to adopt good agricultural practices was observed to be efficient in mitigating the problem of aflatoxins, the receptiveness of farmers to different aspects of the training may have differed. Therefore, in designing an optimized regional aflatoxin contamination strategy, local applicability should be considered.

The aims of this study are to measure the technical efficiency (TE) of Cambodian household’s rice production and trying to determine its main influencing factors using the stochastic frontier production function. The study utilized primary data collected from 301 rice farmers in three selected districts of Battambang by structured questionnaires. The empirical results indicated the level of household rice output varied according to differences in the efficiency of the production processes. The mean TE is 0.34 which means that famers produce 34% of rice at best practice at the current level of production inputs and technology, indicates that rice output has the potential of being increased further by 66% at the same level of inputs if farmers had been technically efficient. Furthermore, between 2013-2015, TE of household’s rice production recorded -14.3% decline rate due to highly affected of drought during dry season of 2015. Moreover, evidence reveals that land, fertilizer, and pesticide are the major influencing input factors of household’s rice production, while disaster, education of household head, family size and other crops’ cultivated area are core influencing factors decreasing TE. Conversely, the main influencing factors increasing TE are irrigated area, number of plot area and sex of household head.

The aims of this study are to measure the technical efficiency (TE) of Cambodian household’s rice production and trying to determine its main influencing factors using the stochastic frontier production function. The study utilized primary data collected from 301 rice farmers in three selected districts of Battambang by structured questionnaires. The empirical results indicated the level of household rice output varied according to differences in the efficiency of production processes. The mean TE is 0.34 which means that famers produce 34% of rice at best practice at the current level of production inputs and technology, indicates that rice output has the potential of being increased further by 66% at the same level of inputs if farmers had been technically efficient. Furthermore, between 2013-2015 TE of household’s rice production recorded -14.3% decline rate due to highly affected of drought during dry season of 2015. Moreover, evidence reveals that land, fertilizer, and pesticide are the major influencing input factors of household’s rice production, while disaster, education of household head, family size and other crops’ cultivated area are core influencing factors decreasing TE. Conversely, the main influencing factors increasing TE are irrigated area, number of plot area and sex of household head.

Rice is the most important food crop in Cambodia and its production is the most organized food production system in the country. The main objective of this study is to measure technical efficiency (TE) of Cambodian rice production and also trying to identify core influencing factors of rice TE at both national and household level, for explaining the possibilities of increasing productivity and profitability of rice, by using translog production function through Stochastic Frontier Analysis (SFA) model. Four-years dataset (2012-2015) generated from the government documents was utilized for the national analysis, while at household-level, the primary three-years data (2013-2015) collected from 301 rice farmers in three selected districts of Battambang province by structured questionnaires was applied. The results indicate that level of rice output varied according to the different level of capital investment in agricultural machineries, total actual harvested area, and technically fertilizers application within provinces, while level of household rice output varied according to the differences in efficiency of production processes, techniques, total annual harvested land, and technically application of fertilizers and pesticides of farmers. The overall mean TE was estimated at 78.4% (national-level) and 34% (household-level), indicates that rice output has the potential of being increased further by 21.6% (national production) and 66% (household) at the same level of inputs and technology if farmers had been technically efficient. The TE also recorded -7% decreasing rate at the national-level and -14.3% at household-level due to highly affected of natural disasters and various environmental and social factors during the study periods.

This research project was funded by the Masterplan for Acceleration and Expansion of Indonesia's Economic Development (MP3EI). The objective of this study was to examine the role of indigenous people of Marind in the agricultural development of rice cultivation in Merauke. A survey was conducted in three villages in Merauke using stratified random sampling. The finding suggest that indigenous people of Marind have a crucial role in the agricultural development of rice cultivation in Papua.

Currently, metal nanoparticles have varied uses for different medical, pharmaceutical, and agricultural applications. Nano-biotechnology combined with green chemistry has great potential for the development of novel and necessary products that benefit human activities, while encourages the reduction of hazardous reagents for nanoparticle production. Green chemistry has an important role due to its contribution to unconventional synthesis methods of gold and silver nanoparticles from plant extracts, which have exhibited antimicrobial potential among other outstanding properties. Biodiversity-rich countries need to collect and convert knowledge from biological resources into processes, compounds, methods, and tools, which need to be achieved along with sustainable use and exploitation of biological diversity. Therefore, this review focuses on the importance of metal nanoparticles, the use of plant extract for their synthesis as well as other available methods, and the relevant antimicrobial activity that can be exploited in a sustainable model of agricultural management through a modern nanotechnological approach.

The beginning of industrialization human being has observed a variety of environmental troubles in the world. This industrialization has not only brought growth and affluence but ultimately troubled the ecosystem. One of the crashes is visible, in form of water contamination. Here the current study heavy metal contamination of water body has been discussed. Effluents from a great number of industries viz., tannery, textile, pigment & dyes, paint, wood processing, petroleum refining, electroplating, leather etc., have a major amount of heavy metals in their wastewater. The conventional technique of handling heavy metal pollution includes chemical oxidation, chemical precipitation, ion exchange, reverse osmosis, membrane separation, electrodialysis etc. These processes are expensive, energy intensive and frequently related with generation of poisonous by-product. Therefore, the adsorption has been examined as a cost-efficient technique of elimination of heavy metals from wastewater. In the current study different low-cost adsorbent has been a review as an abatement of heavy metal contamination from wastewater. These adsorbent comprise materials of natural origin like peat moss, zeolites, clay, and chitin are found to be an effective agent for removal of deadly heavy metals like Pb, Cd, Zn, Cu, Ni, Hg, Cr etc. Separately from these, a variety of agricultural wastes like rice husk, waste tea, neem bark, black gram; Turkish coffee, walnut shell etc. were also known as a powerful adsorbent for heavy metal removal. at the side of that low-cost industrial byproduct like fly ash, lignin, iron (III) hydroxide and red mud, coffee husks, Areca waste, tea factory waste, sugar beet pulp, battery industry waste, blast furnace sludge, waste slurry, sea nodule remains and grape stalk wastes have been discovered for their technical possibility to eliminate toxic heavy metals from impure water.

To better understand farmers’ refusal and corresponding negative emotions to pay agricultural water fee under current policy in rural China, this paper applies mental accounting, a behavioral economics framework, to explore how the governmental policies of reform of rural taxes and fees, direct agricultural subsidy programs and agricultural water fee individually influence farmers’ decisions in paying agricultural water fee. Using fieldwork data from 577 farmers and 20 water managers in Sichuan, we explore farmers’ information processing regarding paying agricultural water fee via three sequential mental accounting processes, with the associated underlying principles and measures behind each process. We find that the information processing in three mental accounting scenarios related to the agricultural water fee elucidates farmers’ observed behaviors in rural China. Generally, in the three mental accounting scenarios, two conditional intuitive expectations and nine conditional intuitive preferences are formed, however, the conditions of those expectations or preferences cannot be matched with the facts due to the reform of rural taxes and fees, the direct agricultural subsidy programs and the internal attributes of agricultural water fee, which interpret those negative behaviors in rural China. Additionally, this paper offers a view into how previous policies create negative psychological externalities (such as farmers’ psychological dependence on the government) through mental accounting to negatively influence agents’ subsequent decision making; it highlights the significance of underlying mental factors and information processing of negative behaviors in policymaking for managing or conserving common pool resources.

Designing and development of agricultural robot is always a challenging issue, because of robot intends to work an unstructured environment and at the same time, it should be safe for the surrounded plants. Therefore, traditional robots cannot meet the high demands of modern challenges, such as working in confined and unstructured workspaces. Based on current issues, we developed a new tomato harvesting wire-driven discrete continuum robot arm with a flexible backbone structure for working in confined and extremely constrained spaces. Moreover, we optimized a tomato detaching process by using newly designed gripper with passive stem cutting function. Moreover, by designing the robot we also developed ripe tomato recognition by using machine learning. This paper explains the proposed continuum robot structure, gripper design, and development of tomato recognition system.

The agriculture and rural areas perform a new function which requires the delivery of public goods to the society. In turn, a comprehensive identification and analysis of agricultural externalities (external economies) involves a need for developing a dedicated valuation methodology. This paper presents the assumptions of the public goods theory and puts them in the context of agriculture. The study focuses on analyzing the valuation methods for external economies, and proposes a methodology for the valuation of some illustrative positive externalities of agricultural production and of natural environment resources regarded as public goods (the agri-tourist value of a farm and the value of a natural waterhole).

A fluid power industry powering the agricultural machinery faces big challenges nowadays. An issue of energy saving has become important due to increasing fuel costs and more stringent emissions regulations impacting vehicle development. A recent study conducted by the U.S. Department of Energy shows that the efficiency of fluid power averages 21 percent. This offers a huge opportunity to improve the current state-of-the-art of fluid power machines, in particular to improve the energy consumption of current applications and create innovative solutions. To increase energy efficiency of fluid power systems reduction of throttling losses and potential energy recovery strategies are needed. Aim of this work is to present classification of current energy saving architectures and aid the development of new techniques for mobile fluid power machines.

Given the cross-scale interactions of agricultural ecosystems, it is important to collect ecosystem service data at the multiple spatial scales they operate at. Mapping of ecosystem services helps to assess their spatial and temporal distribution and is a popular communication tool of their availability and value. For example, maps can be used to quantify distance between areas of available ecosystem services and their beneficiaries and how services fluctuate with changes in land use patterns over time, allowing identification of synergies and trade-offs. However, a lack of local context and too large a resolution can reduce the utility of these maps, whilst masking heterogeneities in access due to equity dynamics. This review identifies and summarizes eight main methods of ESS mapping found in the literature—remote sensing, biophysical modelling, agent based modelling, economic valuation, expert opinion, user preference, participatory mapping, and photo-elicitation. We consider what spatial scales these methods are utilized at and the transferability of data created by each method. The analysis concludes with a methodological framework for mapping ecosystem services, intended to help researchers identify appropriate methods for a multi-scale research design. The framework is exemplified with an overview of a research project in Ethiopia.

The adoption of agricultural technology is an important path for raising agricultural productivity, and thereby for reducing food insecurity and poverty. Despite the efforts to promote adoption in most of the rural areas of Ethiopian farmers, the adoption rate has always been very low. So, it is essential to understand the barriers to adoption. As a result, this study examined the determinants of adoption of multiple agricultural technologies in rural Amhara region of Ethiopia. The study is based on Ethiopian socio-economic survey of 2015/16. A sample of 656 farm households was considered. The paper used multinomial logit model to assess the factors affecting adoption. The result shows that farmers with more educational level, family size, off-farm participation, livestock, extension contact, credit access, advisory service, and farmers closer to plot, all-weather road, zonal town, and farmers with lower remittance income are more likely to adopt new or improved agricultural technology. Therefore, the study recommends the need of policies and interventions on adoption of agricultural technology should pay attention and move along with those variables significantly influencing adoption of agricultural technology.

This paper considered the issue of agricultural fields boundary recognition in satellite images. A novel algorithm based on the aggregated history of vegetation index data obtained via open satellite data, Sentinel-2, was proposed. The proposed algorithm included several basic steps, namely the detection of parcel regions on aggregated index data; the calculation of aggregated edge maps; the segmentation of parcel regions using the edges obtained; the computation of connected components and their contour extraction. In this paper, we showed that the use of aggregated vegetation index data and boundary maps allow for much more accurate agricultural field segmentation compared to the instant vegetation index approach. The quality of segmentation within regions of Russia and the Ukraine was estimated. The dataset that was used and Python implementation of the proposed algorithm were provided.

The aim of the study was to examine the biogas and methane yield of maize silage, a wastes in agricultural point of view and compare it to the biogas productivity of commonly used mixture of maize silage and mixed fodder with water. The experimental study was carried out into 2 parts. Firstly, the batch experiment was conducted in Mesophilic conditions (35°C), at five different hydraulic retention times (HRT): 6 DAT, 11 DAT 20 DAT 29 DAT and 41 DAT. The results revealed that maize silage was generated the highest biogas yield of 537 mL /241 mL at the HRT of 6days /41 days. Mixed fodder produced the highest biogas yield of 421 mL at the HRT of 6th days and the lowest one was 252 mL at the HRT of 20th days. Finally, the methane, carbon dioxide and hydrogen sulphide of biogas produced from maize silage and mixed fodder were analyzed using a gas chromatography. The results shown that biogas from maize silage precedes the gas less than the mixed fodder. In the field level experiment proved that from mixed fodder produced gas which would be enough for 7 member’s family used 3 months for the purpose of their cooking.

This paper investigates and explores the minor crops production in Pakistan and its association with the agricultural gross domestic product. The agriculture sector of Pakistan has a rich contribution to the economic growth and development. Like major crops; minor crops also have a vital role to boost up the agriculture sector. Time span data was used in this study and it was collected from the Economy Survey of Pakistan annual reports. Augmented Dickey-Fuller (ADF) unit root test and Ordinary Least Square (OLS) method was used to analyze the data and results were interpreted by employing the Johansen co-integration test. Study results reveal that bajra, barely and jowar has a significant impact on the agricultural gross domestic product, while the total cropped area has a negative impact on AGDP. On the basis of the study results, we recommend the policy implications.

The access to agricultural information in Sudan continues to be challenging to farmers due to use of inadequate sources and traditional extension approaches. The rapid growth of smart-mobile phones usage in developing countries resulted in several advantages compared to other alternatives in term of costs, geographic coverage and ease of use. This research was conducted in North Kordofan Sate to explore the role of smart-mobile phone in accessing agricultural information. Primary data were obtained by structured questionnaires and focus group discussion through participatory rural appraisal and observation while secondary data were collected from scientific journals, books and authenticated web sources. A number of 230 respondents (10% from total farmers) were interviewed and five focus group discussions were done. Statistical Packages for Social Science (SPSS) version 22 was used to analyze the data with aid of descriptive statistics and Chi-squire Test. The result indicated that most of the respondents fall in age group between 21-40 years, and they depend on farm activity. There was 90% of farmers processed mobile phone since more than three years ago, 90.8%continuedto use smart mobile phone to access agricultural information and showed positive contribution towards income generation. The results also revealed that there was positive perception towards using mobile phones which showed more efficient in use than radio and TVs. The results showed great advantages of using smart mobile phone where 75.2 % of respondents preferred to get agricultural information, logistics and other needs through successful communication in the mid of agricultural season. Results of Chi-squire test showed significant differences between the parameters tested. The study recommended that farmers should be connected with mobile phones to admit ease communication with agricultural extension offices and quick access to their needs and logistics.

Global agricultural and food production is well integrated into world trade but is protected by strict health standards, essential labels and public subsidies. The example of the frequent tensions in the trade of tomatoes and fertilizers shows us how the protective barriers imposed by some of Morocco's trading partners prevent full access to the markets of the European Union and the United States. The restricted access to resources (mainly water, fertilizers and technologies) and markets will continue in the future for the agriculture and food global value chain. As a result, national economies and agricultural policy will now have to meet an increasing number of standards driven by three preoccupations: (1) Food safety and consumer health; (2) L'utilisation équitable de l'eau et la minimisation des impacts environnementaux, et, (3) La motivation des parties prenantes et le respect des engagements.

Over time, the agricultural sector's contribution to the nation's Gross Domestic Product and revenue is declining exponentially; the decline may be attributed to varying problems affecting the agricultural value chain resulting in low productivity. The agricultural value chain comprises a series of activities and value addition processes required to transform raw materials into useable products to maximize the final consumers' utility.¹ The agricultural value chain's marketing activities have been skewed with challenges, thus affecting agricultural productivity and agricultural sector contribution to the economy. The paper critically examines and discuss the impact of agricultural marketing to economic development, a historical review of agricultural marketing in Nigeria, challenges undermining the impact of agricultural marketing, measures to reposition agricultural marketing potential to build back better, policy recommendations to reposition the future of Nigeria's agricultural productivity. The paper aims to promote concerted efforts through knowledge dissemination to build back better through market research and facilitation and boost economic prosperity.

The proportion of area under various crops at a given point in time, known as a cropping pattern, plays an essential role in determining the level of agricultural production. In this study, cropping patterns of three sub-counties in Murang’a County, a typical African smallholder farming area in Kenya, were mapped. Specifically, we compared the performance of eight classification scenarios for mapping cropping patterns; namely using (i) only Sentinel-2 reflectance bands (S2), (ii) S2 and S2 derived vegetation indices (VIs); (iii) S2 and S2 vegetation phenology (VP); (iv) S2 and Sentinel-1 radar backscatter data (S1); (v) S2, VIs, and S1; (vi) S2, VP, and S1; (vii) S2, VIs and VP, and (viii) S2, VIs, VP and S1. Reference data of the dominant cropping patterns and non-croplands were collected. The guided regularized random forest (GRRF) algorithm was used to select the optimum variables and to perform the respective classification for each scenario. The most accurate result of the overall accuracy of 93.16% was attained from the scenario (viii) S2, VIs, VP, and S1. The McNemar’s test of significance did not show significant differences (p≤0.05) among the tested scenarios. Our study demonstrated the strength of GRRF and the synergetic advantage of S2 and S1 derivatives to map cropping patterns in a heterogeneous landscape where high resolution imagery are inaccessible. Our cropping pattern mapping approach can be used in other sites of relatively similar agro-ecological conditions.

Although urbanization has contributed to improving living conditions, it has had negative impacts on the natural environment in the urbanized areas. Urbanization has changed the urban landscape and resulted in increasing land surface temperature (LST). Thus, studies related to LST in various urban environments have become a popular research topic. However, few LST studies focusing on the mountain landscapes (i.e. hill stations) have been carried out. The primary objective of this study is to investigate changes in the landscape and their impacts on LST intensity (LSTI) in the tropical mountain city of Nuwara Eliya, Sri Lanka. The study utilized annual median temperatures extracted from Landsat data collected from 1996 to 2017 based on the Google Earth Engine (GEE) interface. The fractions of built-up (BL), forest (FL), and agricultural (AL) land were calculated using land use and cover maps based on the urban-rural zone (URZ) analysis. The urban-rural margin was demarcated based on the fraction of BL (<10%) and LSTI was measured using the mean LST difference in the urban-rural zone. In addition, the mixture of land use types was calculated using the AL/FL and BL/FL fraction ratios, and grid-based density analysis. The result shows that the BL in all URZ rapidly developed, while AL decreased during the period 1996 to 2017. There was minimal change in the forest area of the Nuwara Eliya owing to the government forest preservation policies. The fraction of the BL increased from 32.4% in 1996 to 58.7% in 2017 in the city center zone (URZ₁) resulting in increased mean LST by 4.7 °C. Furthermore, the increase of the BL/FL fraction ratio and the decrease of the AL/FL fraction ratio were positively correlated with the mean LST. Grid-based analysis showed an increasing positive relationship between mean LST and density of BL. This indicated that BL density has been a crucial element in increasing LST in the study area. The results of this study will be a useful indicator to introduce improved landscape and urban planning in the future to minimize the negative impact of LST on urban sustainability.

Findings from studies of prenatal exposure to pesticides and adverse birth outcomes have been equivocal so far. We examined prenatal exposure to agricultural pesticides in relation to preterm birth and term low birthweight, respectively, in children born between 1998 and 2010 randomly selected from California birth records. We estimated residential exposures to agriculturally applied pesticides within 2 km of residential addresses at birth by pregnancy trimester for 17 individual pesticides and 3 chemical classes (organophosphates, pyrethroids, and carbamates). Among maternal addresses located within 2 km of any agricultural pesticide application, we identified 24,693 preterm and 220,297 term births, and 4,412 term low birthweight and 194,732 term normal birthweight infants. First or second trimester exposures to individual pesticides (e.g., glyphosates, paraquat, imidacloprid) or exposures to 2+ pesticides in the three chemical classes were associated with small increases (3-7%) in risk for preterm birth; associations were stronger for female offspring. We did not find associations between term low birthweight and exposures to pesticides other than for myclobutanil (OR: 1.11; 95% CI: 1.04-1.20) and possibly pyrethroids as a class. Our improved exposure assessment revealed that first and second trimester exposures to pesticides were associated with preterm delivery but few affected term low birthweight.

Cold chain logistics of Agricultural Products demand forecasting can provide the scientific basis for the country to formulate logistics strategy, which further promotes the development of social economy and the improvement of living standards in China. In this paper, a new mathematical combined model is proposed to Agricultural Products Demand. Shandong, one of a China’s province, serves as the main producer and distributor of agricultural products. Based on the index system created from multiple related factors influencing cold chain logistics demand of agricultural products in Shandong, this paper employs principal component analysis to reduce the dimension of various indexes and predicts principal components with time series. Thereafter, multiple linear regression model and neural network model were constructed to forecast the cold chain logistics demand of agricultural products in Shandong, and their combined forecast models were compared. What's more, the paper provides insight for reference and decision-making concerning the development of cold chain logistics industry of agricultural products in Shandong province.

This study aims at providing a systematic and critical review on the state-of-the-art of life cycle applications from the circular economy point of view. In particular, the main objective is to un-derstand how researchers adapt life cycle approaches for the measurement of the empirical cir-cular pathways of agri-food systems along the overall lifespan. To perform the literature review, PRISMA protocol was considered to conduct a review by qualitative synthesis. Specifically, an evaluation matrix has been set up to gather and synthesize research evidence, by classifying pa-pers according to several integrated criteria. The literature search was carried out employing scientific databases. Findings evidence that the most common circularity topics are about closed-loop production systems, i.e. nutrient recovery for agricultural purposes, production of renewable energy, valorization of residues and wastes as fertilizers, food waste, and agro-wastes recycling for agriculture. To evaluate the benefits/impacts of CE strategies, Life Cycle Assess-ment (LCA) proved to be the most common methodology applied by authors, as it can help to meet the main CE requirements slowing and closing resource loops.

Many agricultural water uses crowd the river ecology of the river water, thus leading to irreversible habitat damage. This paper presents an agricultural economic loss calculation model that is based on river ecological basic flow (REBF) protection by introducing a typical crop water requirement coefficient. First, the water balance equation is used according to a set of REBF protection values to compute the agricultural water shortage that results in the REBF. Second, the agricultural water shortage that results in REBF protection and a typical crop water requirement coefficient are used to determine the food production generated by REBF protection. Finally, the loss of food production and the food market prices are used to determine the agricultural economic loss caused by the different protection levels of REBF. A case study of the Weihe River in China is conducted. The calculation model is used to compute the agricultural economic loss on the basis of REBF protection in the Baoji section of the Weihe River, and the change law of the agricultural economic loss that results in different levels of the REBF is discussed. In addition, changes in the canal water use coefficient and the crop structure that affect agricultural economic loss are analyzed. Results show that the spatial and temporal variations in the runoff affect the changes in time and space of the agricultural economic loss. The higher the REBF protection level, the higher the agricultural economic loss. In addition, agricultural economic loss can provide a quantitative basis for reasonable REBF protection. The size of agricultural economic loss helps the government sector in decision-making on REBF protection.

Subsurface drainage systems remove excess water from the soil profile thereby improving crop yields in poorly drained farmland. Knowledge of the position of the buried drain lines is important: 1) to improve understanding of leaching and offsite release of nutrients and pesticides, and 2) for the installation of a new set of drain lines between the old ones for enhanced soil water removal efficiency. Traditional methods of drainage mapping involve the use of tile probes and trenching equipment. While these can be effective, they are also time-consuming, labor-intensive, and invasive, thereby entailing an inherent risk of damaging the drainpipes. Non-invasive geophysical soil sensors provide a potential alternative solution. Previous research has focused on the use of time-domain ground penetrating radar (GPR), with variable success depending on local soil and hydrological conditions and the central frequency of the specific equipment employed. The objectives of this study were 1) to test the use of a stepped-frequency continuous wave (SFCW) 3D-GPR (GeoScope Mk IV 3D-Radar with DXG1820 antenna array) for subsurface drainage mapping, and 2) to evaluate the performance of a 3D-GPR with the use of a single-frequency multi-receiver electromagnetic induction (EMI) sensor (DUALEM) in-combination. The 3D-GPR system offers more flexibility for application to different (sub)surface conditions due to the coverage of wide frequency bandwidth. The EMI sensor simultaneously provides information about the apparent electrical conductivity (EC_a) for different soil volumes, corresponding to different depths. This sensor combination was evaluated on twelve different study sites with various soil types with textures ranging from sand to clay till. While the 3-D GPR showed a high success rate in finding the drainpipes at five sites (sandy, sandy loam, loamy sand, and organic topsoils), the results at the other seven sites were less successful due to limited penetration depth (PD) of the 3D-GPR signal. The results suggest that the electrical conductivity estimates produced by the inversion of EC_a data measured by the DUALEM sensor could be a useful proxy to explain the success achieved by the 3D-GPR in finding the drain lines. The high attenuation of electromagnetic waves in highly conductive media limiting the PD of the 3D-GPR can explain the findings obtained in this research.

The objective of this study is to determine the type of governance of the four main agricultural value chains in Tamaulipas, northeast of Mexico. For the preparation of this research used a qualitative design, using a questionnaire and semi-structured interviews to a sample of representatives of the four selected chains. The results showed that in all the studied networks, control and coordination capacity is limited by the influence that has a link on the rest of the chain. In all cases, was that the industrial link is who leads the chains and exert control over the rest of the links. The results showed that, when the industrial link is located close geographically to the rest of the links (chains of rice and sugar cane), the chains showed a hierarchical type, where the industrial exercised dominion over the rest of the links and captures most of the income. On the other hand, in chains where control is exercised by links that are outside the territory (chains of soybeans and sorghum), they function as captive chains, putting the rest of the chain to its influence and control.

Diversification is an important strategy for managing risk in agricultural systems. Risk analysis can help to support farmers’ diversification strategies, but existing analytical methods are complicated and little used. The minimum regret model helps to fill this gap. It provides a simple, transparent calculation procedure that can be executed with existing spreadsheet software. Regret is an important heuristic in the behavioural sciences and regret-based models are used in finance. The article presents the model with a numerical example. It also presents a framework to compare minimum regret portfolios with two limit cases (maximum utility and minimax regret). A case study illustrates the use of the model and the comparative framework.

This paper studies the impact of agricultural subsidies and product certification on the use rate of green pesticides based on experimental economics. We found that agricultural subsidies effectively increased the utilization rate of green pesticides. If the agricultural subsidies raised from 20% to 100%, the green pesticides’ using rate increased by 438.51%. We also found that product certification increased the utilization rate of green pesticides by 376.16%%.The increase of agricultural subsidies is more effective than the product certification. Under a higher proportion of agricultural subsidies, farmers’ behavior will maintain “status bias”. Therefore, there are three suggestions proposed. Firstly, because of high price of green pesticides and lower production, the subsidies for agricultural materials should raise greatly to effectively improve the utilization rate of green pesticides. It is recommended that green pesticide provided free of charge in some wealthy areas. Secondly, both subsidies and product certification can improve the use rate of green pesticides. However, since the effect of agricultural subsidies is better than product certification, and farmers may have status bias. Therefore, it is recommended to give priority to the substantial increase on the proportion of agricultural subsidies, and then to product certification.

Zingiber cassumunar Roxb. is a powerful medicinal plant that has been used as traditional medicine to cure respiratory problems, pain, and inflammation in China, Indonesia, Thailand and other Asian countries by using the crude extracts. The objective of this research is to identify phytochemical composition of Z. cassumunar Roxb. and to analyze antibacterial activity of crude extract, purified compounds, and their microencapsulated products of Rhizome Z. cassumunar Roxb. Identification of phytochemical composition in crude extract of rhizome Z. cassumunar Roxb. was achieved by chromatography-mass spectrophotometer. The major phytochemical composition in crude extract of Z. cassumunar Roxb. is essential oils, including terpinen4-ol (37.7%), β-pinene (20.8%), and (E)-1-(3,4-dimethoxyphenyl)but-1-ene (13.3%). Crude extract of Z. cassumunar Roxb. was purified with silica gel flash column chromatography, resulting two purified compounds. The antibacterial activity of crude extract, purified compounds, and their microencapsulated products of Rhizome Z. cassumunar Roxb. were evaluated against agricultural and foodborne pathogens by using disc agar diffusion and broth microdilution techniques. All of the samples studied (crude extracts, purified compounds, and microencapsulated of Z. cassumunar Roxb.) were effective against all the bacteria. Based on the results of the disc-diffusion assay suggested that amongst the samples studied, purified compounds (compound 1 and 2) and microencapsulated purified compounds (compound 1 and 2) exhibited more effective against all the bacteria compared to the crude extracts. Antibacterial activity of the rhizome of Z. cassumunar Roxb. was contributed mainly by the essential oils components as the active compounds. Gram-negative bacteria (X. oryzae, X. translucens, Pseudomonas spp, E. coli, and S. typhimurium) appeared to the most resistant to the crude extracts, purified compounds, and microencapsulated of Z. cassumunar Roxb. compared to the gram-positive bacteria (S. aureus, B. cereus, and L. monocytogenes). Microencapsulated of the tested samples (crude extract, purified compound 1, and purified compound 2) of the rhizome Z. cassumunar Roxb. exhibited high antibacterial activity with no significantly different with the tested samples without microencapsulation. These results suggest potential antibacterial properties of Z. cassumunar Roxb., which useful for agricultural plant health, food preservation, natural therapies, and pharmaceuticals.

The objective was to analyze the effects of adding anthocyanin delphinidin-3-O-sambubioside and cyanidin-3-O-sambubioside of Hibiscus sabdariffa L. in animal diets. Scientific articles published before 2021 in clinics, pharmacology, nutrition, and animal production were included. The grains/concentrate, metabolic exigency, and caloric stress contribute to increasing the reactive oxygen species (ROS); the excess of ROS unbalance the oxidants and antioxidants. Cyanidin-3-O-sambubioside and delphinidin-3-O-sambubioside have antioxidant, antibacterial, antiviral, and anthelmintic activities. In the rumen, anthocyanin might show interactions and/or synergisms with substrates, microorganisms, and enzymes which could reduce the fiber degradability, but increase the potential methane (CH4) emissions; since anthocyanin interferes in the biohydrogenation of fats, they increase the fat milk and meat quality. Anthocyanins reduce plasma oxidation and deposit in tissues, increasing the milk and meat antioxidant activities. Cyanidin-3-O-sambubioside and delphinidin-3-O-sambubioside act as inhibitors of the angiotensin-converting enzyme (ACEi) and rennin expression which may improve milk yield (there is not enough evidence in ruminants, though). Polyphenols affect the reproductive potential. Sub products of HS contain as many amounts of polyphenols as calyces, and their inclusion in diets would positively affect the average daily gain and fat meat quality. Including HS in ruminant diets can improve the meat and milk quality.

Due to the shortage of agricultural labor forces and rapid aging of farmers, the utilization of tractors is becoming popular and essential in Korea. Tractors can be classified into two types, a walking tractor called as a power tiller and a riding tractor. In this study, agricultural tractors including walking and riding types were categorized into 4 levels by rated output power. And diesel emission inventory of tractors was established and analyzed using 2011 and 2019 survey data in Korea. Emission inventory including CO, NOx, SOx, TSP(PM10), PM2.5, VOCs and NH3 were established using Tier 3 methodology. The total amount of emission using agricultural tractors was decreased about 13% from 2011 to 2019. The number of walking tractors were decreased by about 19% in 8 years, on the other hand that of riding tractors were increased by about 12%. However, the emission reduction is about 48% for walking tractors and the emission increment is about 5% for riding tractors. Thus, the total emission from agricultural tractors was decreased by about 16% in those periods. It is due to the decrease of 21% and 15% in the hours of use of walking and riding tractors, respectively, in 2019. Walking tractors mainly emit air pollutants from spraying and transporting. Riding tractors mainly 61% of total air pollutants emits from soil preparation and transporting operations. Geographic information system (GIS) was used to spatially assign air pollutants variables into 17 provinces and metropolitan cities in Korea. High emission generating regions and changes of emissions during 8 years were clearly seen in GIS analysis. High air pollutant emitting regions are mainly located in the western and southern regions of Korea, which have plenty of arable areas compared to other regions in Korea.

Agricultural land conversion (ALC) is an incentive–driven process. In this paper we further investigate the inter–relationship between land economic value (LEV) and ALC. To achieve this goal, we calculated LEV for agricultural and non-agricultural (housing) uses in two areas in East Java, Indonesia. The first area represents suburban agriculture, facing rapid urbanization and experiencing high rate of ALC. The second area represents rural agriculture with zero ALC. Furthermore, we identified factors affecting LEV in both areas for both uses. The resut of this study show that agricultural land yielded higher economic benefit in rural area. Conversely, comparing to agricultural land, housing creates 7 times higher value in urban area. Moreover, agricultural land shown to create higher profit after converted. Ironically, the similar comparison doesn’t exists in rural area. Agricultural land only yielded 19% more value, indicate that agricultural land can be easily converted. It is also proven by the growing number of new urban core in the periphery area. There are several factors affecting land economic value, for agricultural use, soil fertility, accessibility, and cropping pattern are important variables. While accessibility and location in urban area increases land value for housing.

As the EU is moving towards a low carbon economy and seeks to further develop its renewable energy policy, this paper quantitatively investigates the impact of plausible energy market reforms from the perspective of bio-renewables. Employing a state-of-the-art biobased variant of a computable general equilibrium model, this study assesses the perceived medium-term benefits, risks and trade-offs which arise from an advanced biofuels plan, two exploratory scenarios of a more 'sustainable' conventional biofuels plan and a 'no-mandate' scenario. Consistent with more recent studies, none of the scenarios considered present significant challenges to EU food-security or agricultural land usage. An illustrative advanced biofuels plan simulation requires non-trivial public support to implement whilst a degree of competition for biomass with (high-value) advanced biomass material industries is observed. On the other hand, it significantly alleviates land use pressures, whilst lignocellulose biomass prices are not expected to increase to unsustainable levels. Clearly, these observations are subject to assumptions on technological change, sustainable biomass limits, expected trends in fossil fuel prices and EU access to third-country trade. With these same caveats in mind, the switch to increased bioethanol production does not result in significant market tensions in biomass markets.

In the past few decades, the use of agricultural waste has been increasing due to the increasing demand for sustainable materials. This paper aims to review the significance of agricultural waste as a type of wealth and its potential as an alternative material in the composite field. It was found that many agricultural wastes had been utilized in various production of ecological products. The review discusses the physical, mechanical, and chemical properties of the natural fibers made from agricultural waste and the use of Pineapple Leaf Fiber Reinforced Composite to manufacture UAVs (Unmanned Aerial Vehicles). The extracted waste fibers’ physical, mechanical, and chemical properties show that these agricultural wastes can produce better advanced composite materials suitable for structural applications. The use of pineapple leaf fiber in Unmanned Aerial Vehicles manufacturing has the potential to revolutionize this industry and contribute to a more sustainable future.

Changes in agricultural yields due to climate change will affect land use, agricultural production volume, and food prices as well as macroeconomic indicators, such as GDP which is important as it enables one to compare the climate change impacts across multiple sectors. This study considered five key uncertainty factors and estimated macroeconomic impacts due to crop yield changes using a novel integrated assessment framework. The five factors are 1) land-use change (or yield aggregation method based on spatially-explicit information), 2) the amplitude of the CO2 fertilization effect, 3) the use of different climate models, 4) socioeconomic assumptions and 5) the level of mitigation stringency. We found that their global impacts on the macroeconomic indicator value were 0.02 - 0.06% of GDP in 2100. However, the impacts on the agricultural sector varied greatly by socioeconomic assumption. The relative contributions of these factors to the total uncertainty in the projected macroeconomic indicator value were greater in a pessimistic world scenario characterized by a large population increase and low income (0.6%) than in an optimistic scenario (0.00%).

This work analyzes the visibility and scientific impact of publications related to agricultural value chains. The incidence of bibliometric indicators allows for the interpretation of bibliographic information generated worldwide. Objective: The objective of this research is to analyze the published literature and bibliometric indicators on agricultural value chains. The Web of Science database was used to extract value chains data. The study analyzed articles published between 2010 and 2020. The keywords used are "agricultural value chains'' and articles from journals or studies related to the subject were selected for bibliometric analysis and methodological review. In the search for the keyword, a total of 4208 results were extracted, of which 1,669 records were considered for analysis. The bibliometric analysis of the data reveals that Wageningen University (55) has the highest number of publications, followed by Chinese Acad Sci (26). The author Klerkx L (9) has the highest number of records, followed by Hellin J (7). With respect to the countries with the greatest contributions on the subject are: the People's Republic of China, Germany, Italy, France and the United States. The study contributes to the analysis of bibliometrics and provides a methodological review of published journal articles on agricultural value chains. This bibliographic study presents the history of research development in agricultural value chains.

Coronavirus is now a significant human pathogen with the emergence of SARS-CoV-2. Until now there has been no data to support a threat to agricultural industries. Using a comparative genomic protein analysis, this study examined the angiotensin-converting enzyme II (ACEII) gene of 17 animal species with an emphasis on agriculture. To determine viral vulnerability the 20 known SARS-CoV-2 receptor-binding domain (RBD)/ACEII receptor interaction sites were compared to determine their potential susceptibility to the SARS-CoV-2 virus. With the known bat host’s (XP_032963186) number of binding sites as a threshold, we note that ALL animal species examined in this study contained significant numbers (≥10) of SARS-CoV-2 binding sites and could be at risk for SARS-CoV-2 infection. The data from this study suggest SARS-CoV-2 imposes a grave threat to the safety and security of the agricultural industry. Urgent studies are needed to determine if infected animals can transmit SARS-CoV-2 before and/or after processing.

Precision agriculture (PA) is a holistic, sustainable, innovative systems approach that assists farmers in production management. Adopting PA could improve sustainable food security and community economic sustainability. Developing an understanding of PA adoption attributes is needed to assist extension practitioners to promote adoption and better understand the innovation adoption phenomena. A systematic review of literature was conducted to investigate PA adoption. Thirty-three publications were examined, and four themes were found among the reviewed publications. The results were interpreted using Rogers’ diffusion of innovations framework to address the research objectives. Of the reviewed literature, we found relative advantage and compatibility were two dominant attributes to strengthen the adoption of PA, and the complexity attribute was rarely used to promote the adoption of PA. This study shows that change agents do not fully use five attributes of innovation when they promote PA technology to stakeholders to adopt. Thus, we recommend studies from the agricultural extension specialists’ perspectives in the future may determine contributions to motivate farmers’ adoption of PA, in particular related to complexity.

In Korea, rice is a major staple grain and is mainly cultivated using various agricultural machinery. Air pollutants emitted from agricultural machinery have their origins mainly from the exhaustion of internal combustion engines. In this study, emission characteristics of five main air pollutants by European Environment Agency's Tier 1 method for rice cultivation was analyzed. Diesel is a main fuel for agricultural machinery and gasoline is generally used only for rice transplanters as a fuel in Korea. Tractors consume 46% of total fuel consumption and 56% of diesel fuel consumption. Gasoline used for rice transplanters accounts for 17% of total fuel consumption each year. Tractors and rice transplanters are emitting 82% of all total pollutants. From 2011 to 2019, the total amount of air pollutant emissions was decrease by 15%. That accounted for the reduction of rice cultivation fields in those periods. Rice transplanting operation was in charge of 42% of total emissions. Then, harrowing, harvesting, tilling, leveling, and pest control operations generated 10%, 10%, 8%, 8% and 7% of total emissions, respectively. The contribution of each air pollutant held 54% of CO, 39% of NOx, 5% of NMVOC, and 2% of TSP from the total emission inventory. The three major regions emitting air pollutants from mechanized agricultural practices were Jeollanam-do, Chungcheongnam-do, and Jeollabuk-do, which consume 55% of total fuel usage in rice farming. The total amount of air pollutant emissions from rice cultivation practices in 2019 was calculated as 8,448 Mg in Korea.

The sustainability of agriculture depends as much on the natural resources required for production as it does on the stakeholders that manage those resources. It is thus essential to understand the variables that influence the decision-making process of agricultural stakeholders to design educational programs, interventions, and policies geared towards their specific needs, a required step to enhance agricultural sustainability. We examined the perceptions, experiences, and priorities that influence management decisions of five major groups of agricultural stakeholders (conventional small grain producers, organic small grain producers, organic vegetable producers, extension agents and agro-industry crop consultants, and researchers) across the Montana, United States. Results revealed that while stakeholder groups have distinct perceptions, experiences, and priorities, there were similarities across groups. Specifically, organic vegetable and organic small grain producers showed similar responses that were, in turn, divergent of conventional producers, researchers, and crop consultants. Conventional small grain producers and researchers showed overlapping response patterns while crop consultants formed an isolated group. Our results reinforce the need for agricultural education and programs that address unique and shared experiences, priorities, and concerns of multiple stakeholder groups. This study endorses the call for a paradigm shift from the traditional top-down agricultural extension model to one that accounts for participants’ socio-ecological contexts to facilitate the adoption of sustainable agricultural systems that support environmental and human wellbeing.

Conflict between agricultural water resources and socio-economy development is a global problem. Accurate evaluation of coordinated development of agricultural water resources and socio-economy and risk mitigation is necessary for sustainable development. An evaluation method, including selection of criteria, data collection, determination of weight, evaluation of coordinated development, prediction of parameters, and judgment of coordinated development state, has been proposed to study coordinated development degree. To deal with uncertainties, Monte Carlo method and fuzzy set method were used. The method is demonstrated to solve a real-world evaluation problem in Shanxi Province in the middle of China. Results show that coordinated development degrees were (0.7, 0.8) for most of the cities of Shanxi in 2015, indicating that coordinate development state was intermediate coordinate. To achieve balanced development, more attention should be put on socio-economic development in Taiyuan and Yanquan, and agricultural water resources utilization in Jinzhong, Yuncheng and Xinzhou. The average coordinated development degree is 0.758, and coordinate development state was intermediate coordinate from 2006 to 2015. Coordinated development degree has a trend of decreasing markedly, coordinate development state will be barely coordinated, and agricultural water resources utilization lags behind socio-economic development in 2020. The study demonstrates the practicability of the improved method, by evaluating coordinated development degree under uncertainty and forecasting future risks, which will conduce to promote sustainable development of agricultural water resources and socio-economy.

Rice production in the Tonle Sap basin is one of the main drivers for economic and social development in Cambodia. The Tonle Sap basin has experienced many different forms of disasters while more attention has been drawn to drought disaster. The objective of this study is to assess the impacts of drought on agriculture and food security through a case study of the Baribo basin, a sub-basin of Tonle Sap basin, Cambodia. Ground observations and satellite-based products were used for drought assessment from 1985 to 2008 which was the period with relatively good data quality. The Standardized Precipitation Index (SPI) and Standard Vegetation Index (SVI) were selected for meteorological and agricultural droughts assessment, correspondingly. Both SPI and SVI consistently suggested that drought is a major natural hazard causing food insecurity in the target basin. The highest drought intensity (DI) and severity (DS) occurred between 1993-1994 and the longest drought duration (DD) occurred between 2002 and 2006. The most severe damage to rice production was in 2004, affecting about 46% of the total cultivated area. The analysis showed that drought duration had a strong relationship with the affected area growing rice as well as food insecurity in the Tonle Sap basin.

Efficient use of agricultural wastes and by-products, basically transforming waste materials into value added products, is considered as pivotal for an effective bioeconomy strategy for the rural development. To this scope, citrus waste management represents a major issue for citrus processors but, at the same time, it embodies a potentially unexploited resource for the rural sustainable development. This study focuses on analyzing the current management of citrus waste in South Italy and identifying the determinants and barriers that may affect an entrepreneur’s choice on the destination of citrus waste. Citrus processors’ preferences about the contract characteristics needed to take part in a co-investment scheme have been investigated. Both analyses are preliminary steps to design an innovative and sustainable citrus by-product supply chain. Results show that the distance between the citrus processors and the citrus by-products plant is one of the main criteria for choosing alternative valorization pathways. Moreover, the required guaranteed capital, the short duration of the contract, and low risk are contract scheme characteristics that improve entrepreneurs’ willingness to co-invest in the development of a citrus waste multifunctional plant.

The issue of measurement reliability is certainly undervalued in the local agricultural marketplace organization. Besides, there was nothing identified concerning the extent of cereal commodity trade measurement cost. Hence, this paper aimed to estimate the magnitude of cereals trade quantity measurement cost caused by instrument error and unreliability; mainly by targeting the local marketplace in Ethiopia. The survey was conducted in six different districts’ marketplaces (n = 602) of Oromia region by employing administered structured questionnaires and site mass measurement calibration. The gathered data were analyzed using independent samples t-test, one sample t-test, and analysis of variance. According to the findings, the actual value measurement means of the quantity of most local units of the farmers were greater than small traders. The independent t-test result indicated that the average values of the quantity of the majority of units of measurement between farmers and small traders were varied significantly. Estimated measuring instruments’ error and unreliability associated average measurement cost of one-day market transaction over total cereals marketed amount were higher comparatively for a sack, bowl, and glass units. This study demonstrates that homogeneity in measurement, policy, and institutions that aid cereals trade would have an indispensable role to reduce measurement costs and ensure equitable exchange.

Agricultural land fires have been linked to various and adverse impacts on ecosystems, food security and the agriculture sector. Understanding the patterns and drivers of agricultural land fires is essential for effective agricultural land fire management. The key objectives of this study were to (1) analyze the temporal and spatial patterns of agricultural land fires using satellite remote sensed data, (2) assess a range of environmental conditions that could drive the occurrence of agricultural land fires, (3) determine the best model for predicting agricultural land fires and (4) determine the relative contribution of each environmental condition variable on the best predictive model. We used both univariate and multivariate regressions for the fire prediction capability of four independent environmental conditions (fuel, weather, topographic and anthropogenic). Analysis of historical satellite data revealed that agricultural land fires were more frequent than forested land fires. Our analyses also revealed that fuel condition was the most important variable for predicting agricultural land fires followed by weather, topographic and anthropogenic conditions. This study provides a novel multivariate model for predicting agricultural land fires that harbors the potential to improve agricultural land fire management and reduce fire risk within the agricultural sector.

Following the development and dissemination of new climate-smart agricultural technologies to farmers globally, there has been an increase in the number of socio-economic studies on the adoption of climate-smart integrated pests’ management (CS-IPM) technologies over the years. In this study, we review empirical evidence on adoption determinants of CS-IPM technologies and identify possible science-policy interfaces. Generally, our review shows that socioeconomic and institutional factors are influential in shaping CS-IPM adoption decisions of farmers. More specifically, income was found to positively influence the adoption of CS-IPM technologies while land size owned influences CS-IPM adoption negatively. Registered land tenure (registered secure rights) positively influences CS-IPM technologies’ adoption, implying that efficient land markets that enable competitive and fair distribution and access to land, more so by the vulnerable but efficient smallholder producers do indeed increase the adoption of CS-IPMs technologies. Social capital, achieved via farmers’ organizations is also central in fostering CS-IPM technologies’ adoption, just as markets reforms that minimize market failures regarding access to credit, labor, and agricultural information, which could indirectly hinder farmers’ use of CS-IPM practices. Functional extension systems that improve farmers’ awareness of CS-IPM do also improve CS-IPM technologies’ adoption. However, the adoption of CS-IPM technologies in Ghana and Benin is slow-paced because of factors like lack of access to farm inputs that facilitate uptake of these technologies, lack of credit facilities, and limited extension services among others. Interestingly, our review confirms that CS-IPM technologies do indeed reduce and minimize the intensity of pesticide usage and foster ecosystem (environmental and human) health. Therefore, this review unearths strategic determinants of CS-IPM adoption and makes fundamental guidance around climate-smart innovations transfer and environmental policies that should be prioritized to curb environmental pollution and ensure agricultural ecosystems’ sustainability.

Preparation of a low-cost cellulose-based bioadsorbent from the cellulosic material extracted from the rose stems (CRS) was carried out; rose stems were considered agricultural wastes. After the required pretreatment of this waste, and its further treatment with an acidic mixture of acetic and nitric acids, the CRS product was yielded. The resulting bioadsorbent was characterized by several techniques, such as X-ray diffraction, which revealed diffraction maxima related to cellulose structure, whose calculated crystallinity index (CrI) was 75 %. In addition, Fourier Transform Infrared spectroscopy (FTIR), 13C Nuclear Magnetic Resonance (NMR), and X-ray Photoelectron Spectroscopy (XPS) showed signs of acetylation of the sample, also, the thermal properties of the solid was evaluated through Thermogravimetric Analysis (TGA). Scanning Electron Microscopy (SEM) showed cellulose fibers before and after the adsorption process, some particles with not regular shapes were also observed. The CRS bioadsorbent was used in the effective adsorption of valuable Tb(III) from aqueous solution. The adsorption data resulted in a better fit to the Freundlich isotherm, and pseudo-second-order kinetic models; however, chemisorption had not been ruled out. Finally, desorption experiments revealed a recovery of terbium ions with an efficiency of 97 % from terbium-loaded bioadsorbent.

Recent advancements of computer and electronic systems have motivated the extensive use of intelligent systems for automation of agricultural industries. In this study, the temperature variation of the mushroom growing room is modeled through using a multi-layered perceptron (MLP) and radial basis function networks. Modeling has been done based on the independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP is found to be the second repetition with 12 neurons in the hidden layer and 20 neurons in the hidden layer for radial basis function networks. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural networks with radial basis function was selected as the optimal predictor for the behavior of the system.

The purpose of this paper is to analyze market reflexivity in agricultural futures contracts with different maturities. To this end, we apply a four-dimensional Hawkes model to storable and non-storable agricultural commodities. We find market reflexivity for both storable and non-storable commodities. Reflexivity accounts for about 50 to 70 percent of the total trading activity. Differences between nearby and deferred contracts are less pronounced for non-storable than for storable commodities. We conclude that the co-existence of exogenous and endogenous price dynamics does not change qualitative characteristics of the price discovery process that have been observed earlier without consideration of market reflexivity.

Every intervention of planning, implementation, and monitoring of agricultural and agri-environmental policies requires assessment tools that should have the characteristics of relevance, completeness, interpretability, data quality, efficiency, and overlapping. Despite the extensive selection of bibliographies and numerous projects designed to develop agri-environmental indicators necessary for assessing the sustainability of new policies, it is difficult to have an integrated and updated set of indicators available, which can be an effective and practical application tool to assists policymakers, researchers, and actors in policy design, monitoring and impact assessment. Particularly, such need is pressing to face the new environmental challenges imposed by the upcoming European Union Green Deal on the Common Agricultural Policy (CAP) post 2023. This study, therefore, aims to fill this gap by proposing a selection methodology and different pools of agri-environmental indicators differentiated based on a scale approach (crop-farm-district-region). Furthermore, we have attempted to validate our approach by quantifying selected indicators for a specific evaluation necessity, represented in this case by an assessment of environmental impact of land use change induced by CAP greening requirements in the Northern Italy context. Results of this validation show original crops’ impacts comparison, but also highlight great knowledge gaps in the available literature.

In the Brazilian Amazon, rural settlements are increasingly isolated by large-scale production farms, jeopardizing their sustainability and the good living of family farmers. Works were carried out in settlements to measure sustainability. However, the majority does not consider the participation and the collectively of those involved. In this way, we propose to evaluate, in a collective and participatory way, the sustainability and good living of the SDP São Paulo Rural Settlement, of the northern Amazon of Mato Grosso. We used the didactic-pedagogical method Circle of Sustainability, developed from five points: 1st - circle of investigation of generating themes; 2nd - circle of the history of the subject world; 3rd - circle of diagnosis of rural settlements; 4th - circle of exchange of knowledge; and 5th - circle of sustainable perceptions and narratives. The historical, socioeconomic and cultural characterization of the settlement allowed us to understand how sustainability and good living are being built in the settlement history process. Sustainability and good living are dialectical processes, are under construction, in movement.

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.

The post-2007 crisis-induced an agricultural expansion across Africa, but local level production and marketing experiences remain understudied. This study assesses the dynamics of agricultural expansion and small-scale farmers’ experiences in rural Zambia. Using a mixed research design, data were drawn from surveys, multi-level interviews, group discussions policy reviews and observations. Results show an agriculture expansion among small-scale farmers is underway due to favourable climatic conditions, land, and water availability, enabled by state subsidies, and an emerging market in commercial and supermarket outlets. However, farmers encounter production and marketing challenges related to poor tenure security, late delivery of inputs, and low financing. They face low and fluctuating prices, poor infrastructure, including low levels of mechanisation necessary to expand the production. Overall, despite an agricultural and land-use expansion taking shape, actual benefits for real transformation are largely missing – and currently over their heads. An argument is made that whilst policy actors continue to impress farmers to organise themselves to maximise benefits of an agricultural expansion, actual processes on how farmers can achieve this are missing in policy and practice. This necessitates a focus on multi-level processes aimed at addressing production, storage and marketing dynamics within a progressive coordination arrangement that centralizes small producers. Until that is addressed, the prospects for local development and poverty reduction for small-scale farmers under an agricultural expansion will be slender but continue to centrally reside in the state efforts to create enabling local and community environment that addresses real challenges. Overall, this study helps to extend the debate on diverse processes shaping rural transformation in Zambia and across sub-Saharan Africa, including the role and importance of agricultural expansion.

Achieving eco-efficiency in agriculture production at low environmental costs is key to sustainable agriculture. Using the DEA-SBM model, this study evaluated the agricultural eco-efficiency of the 77 counties and districts in China’s Jiangsu province from 1999 to 2018 and analyzed its spatio-temporal evolution pattern and influencing factors. The mains conclusions were as follows: (1) The overall agricultural eco-efficiency and its decomposition terms, pure technology efficiency and scale efficiency, exhibited a fluctuating downward trend. The regional inequality in agricultural eco-efficiency had been widening and exhibited a strong positive spatial association. (2) The agricultural eco-efficiency in Jiangsu province presented a “high south and low north” spatial pattern. High-level agricultural eco-efficiency areas were in the Taihu Plain in Sunan, while low-level agricultural eco-efficiency zones are distributed across Subei City. The High-High-type spatial association pattern is concentrated in the Suzhou-Wuxi-Changzhou region, while the Low-Low areas are mainly in the coastal regions of Subei and Suzhong. (3) The spatial pattern of PTE and SE generally exhibited a “high south and low north” distribution. Areas with positive growth in agricultural eco-efficiency, PTE, and SE, were situated in Xuzhou, Nanjing city, and the bordering regions between Yangzhou and Huai’an, and Changzhou and Wuxi. (4) The excessive redundant use and application of pesticides, chemical fertilizer, agricultural diesel, labor, land, and agricultural carbon emission have been the primary factor affecting Jiangsu's agricultural eco-efficiency. Irrigation had also signficantly impacted agricultural eco-efficiency, while mechanical power and agricultural film had minimal effect. The majority of counties and districts in Subei, Suzhong, and Ningzhen Yang Hilly region have issues regarding their excessive usage of chemical fertilizer, pesticide, chemical fertilizer, agricultural diesel, labor, and land. The findings of this study can contribute towards a better understanding of agricultural eco-efficiency and spatial association effect and can help policymakers increase agricultural eco-efficiency.

: For decades the city has created food models by requiring the countryside to meet the growing demand with increasingly more homologated crop reconversions and increasingly vast and competitive farms. The current acceleration of the land concentration process and the dramatic experience of the COVID 19 pandemic have, however, forced us to redefine the city-country relationship, which has been called into question for some years now in various FAO and EU documents. Based on the GECOAGRI-LANDITALY survey itinerary, a proven tool for reading local peculiarities, the AAs show how easy it is to recognize which farmlands can best fulfil the role of guaranteeing food safety and protecting the quality and typicality of traditional foods. The final proposal is to start a new agri-food policy that no longer starts from the demand formulated by the city but, reversing the direction of the old relationship, it starts from the availability of products offered by the countryside to re-educate consumption and promote the sustainability of agricultural practices.

Farm fragmentation is the occurrence of numerous and often discontinuous land parcels associated with a single farm. Farm fragmentation is considered to be a defining feature of Northern Ireland’s (NI) agricultural landscape, influencing agricultural efficiency, productivity, and the spread of livestock diseases. Despite this, the full extent of farm fragmentation in cattle farms is not well understood, and little is known of how farm fragmentation either influences, or is influenced by, different animal production types. This study describes and quantifies farm fragmentation metrics for cattle enterprises in NI, presented separately for dairy and non-dairy production types. We find that 35% of farms consist of five or more fragments, with larger farms associated with greater levels of farm fragmentation, fragment dispersal and contact with contiguous farms. Moreover, this was particularly evident in dairy farms, which were over twice the size of farms associated with non-dairy production types, with twice as many individual land parcels and twice as many fragments. We hypothesise that the difference in farm fragmentation and farm size between dairy and non-dairy production types is associated with the recent expansion of dairy farms after the abolition of the milk quota system in 2015, which may have driven the expansion of dairy farms via the acquisition of land. The high levels of land fragmentation, fragment dispersal and contiguous contact observed in NI cattle farms may also have important implications for agricultural productivity and epidemiology alike. Whilst highly connected pastures could facilitate the dissemination of disease, highly fragmented and parcellised land could also hamper productivity via diseconomies of scale, such as preventing the increase of herd sizes or additionally, adding to farm costs by increasing the complexity of herd management.

The ultimate goal of this paper was to examine the degree of elasticity between two variables, namely, agricultural credit and agricultural growth, in Angola in the period 2003–2022. Time series data were fitted into the ARDL test using various econometric techniques such as the ADF stationarity test, Granger causality and the ordinary least squares method as well as a vector error correction model (VECM) to analyze the relationship between agricultural credit and agricultural economic growth, showing a causal relationship. Both the impacts through elasticities and the optimal point existing in this relationship were estimated. It was concluded that the impact of agricultural credit on agricultural GDP was 14.41%. Granger causality shows signs of a positive linkage between agricultural credit and agricultural GDP. However, there is a causal relationship between agricultural credit and agricultural GDP, in a unidirectional aspect. This result is consistent with most of the earlier studies reviewed in the literature, confirming that credit-oriented monetary policies can boost economic growth and, consequently, development in Angola. It is important for agricultural credit systems to be designed in a way that ensures equitable access, fair interest rates, and appropriate risk management mechanisms. Additionally, monitoring and evaluation mechanisms should be in place to assess the environmental and social impacts of credit programs on agricultural sustainability. It is worth noting that this is a first-of-a-kind study on the matter of the Angolan credit experience, specifically for the agricultural sector. Angola is still searching for a sustainable credit model that could be used as a catalyzer to boost growth and contribute to economic development.

Traditional agricultural extension services rely on extension workers, especially in countries with large agricultural areas. In order to increase adoption of sustainable agriculture, the recommendations given by such services must be adapted to local conditions and be provided in a timely manner. The AgroTutor mobile application was built to provide highly specific and timely agricultural recommendations to farmers across Mexico and complement the work of extension agents. At the same time, AgroTutor provides direct contributions to the United Nations Sustainable Development Goals, either by advancing their implementation or providing local data systems to measure and monitor specific indicators such as the proportion of agricultural area under productive and sustainable agriculture. The application is freely available and allows farmers to geo-locate and register plots and the crops grown there, using the phone’s in-built GPS, or alternatively, on top of very high-resolution imagery. Once a crop and some basic data such as planting date and cultivar type have been registered, the app provides targeted information such as weather, potential and historical yield, financial benchmarking information, data-driven recommendations as well as commodity price forecasts. Farmers are also encouraged to contribute in-situ information, e.g., soils, management, and yield data. The information can then be used by crop models, which, in turn, would send tailored results back to the farmers. Initial feedback from farmers and extension agents has already improved some of the app’s characteristics. More enhancements are planned for inclusion in the future to increase the app’s function as a decision support tool.

Economic globalisation has promoted the extensive circulation of commodities around the world, and international trade has increasingly been a strong booster for economic growth in China. As an foundation for national economy, the shift of agricultural products (AP) from production to distribution has become the focus of agricultural development, and in recent years, the international trade of agricultural products in China has been the highest in the world. While, cross-border e-commerce (CBE), as one of the important modes of agricultural products circulation, has brought new opportunities for China's fresh agricultural products to carry out online import and export trade under the favourable policies continuously introduced internationally. This research analyses status of APCBE in Yunnan Province, China, and propose strategies for developing CBE of Yunnan AP.

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

Changes in agricultural policy may have rapid impact even on landscapes which have taken millennia to form. Here we explore the potential of UK leaving the EU as a catalyst for profound changes in pastoral landscapes in Wales. Impending change of the trading regime governing agricultural produce, concurrent to public pressure to use agricultural subsidies for environmental goals, may lead to unforeseen consequences for Welsh natural environment. We employ a combination of change demand modelling and ‘story and simulation approach’ to predict the effect of five hypothetical scenarios on land use and land use change in Wales by 2030. We show that the most extreme trade scenario would result in a near-uniform distribution of broadleaf woodland across most of Wales. Abandonment of marginal and low productivity grazing would likely give way to afforestation, initiating a return to forested landscapes not seen in Wales for several thousands of years.

Hydrogen sulfide and ammonia are always a concern in the livestock industries, especially when farmers try to clear their manure storage pits. Agitation of manure can cause dangerously high concentrations of harmful agents such as H₂S and NH₃ to be emitted into the air. Biochar has the ability to sorb these gases. We hypothesized that applying biochar on top of manure can create an effective barrier to protect farmers and animals from exposure to NH₃ and H₂S. In this study, two kinds of biochar were tested, highly alkaline, and porous (HAP, pH 9.2) biochar made from corn stover and red oak biochar (RO, pH 7.5). Two scenarios of (6 mm) 0.25” and (12 mm) 0.5” thick layers of biochar treatments were topically applied to the manure and tested on a pilot-scale setup, simulating a deep pit storage. Each setup experienced 3-min of agitation using a transfer pump, and measurements of the concentrations of NH₃ and H₂S were taken in real-time and measured until the concentration stabilized after the sharp increase in concentration due to agitation. The results were compared with the control in the following 3 situations: 1. The maximum (peak) flux 2. Total emission from the start of agitation until the concentration stabilized, and 3. The total emission during the 3 min of agitation. For NH₃, 0.5” HAP biochar treatment significantly (p<0.05) reduced maximum flux by 63.3%, overall total emission by 70%, and total emissions during the 3-min agitation by 85.2%; 0.25” HAP biochar treatment significantly (p<0.05) reduced maximum flux by 75.7%, overall, total emission by 74.5%, and total emissions during the 3-min agitation by 77.8%. 0.5” RO biochar treatment significantly reduced max by 8.8%, overall total emission by 52.9%, and total emission during 3-min agitation by 56.8%; 0.25” RO biochar treatment significantly reduced max by 61.3%, overall total emission by 86.1%, and total emission during 3-min agitation by 62.7%. For H₂S, 0.5” HAP biochar treatment reduced the max by 42.5% (p=0.125), overall total emission by 17.9% (p=0.290), and significantly reduced the total emission during 3-min agitation by 70.4%; 0.25” HAP treatment reduced max by 60.6% (p=0.058), and significantly reduced overall and 3-min agitation’s total emission by 64.4% and 66.6%, respectively. 0.5” RO biochar treatment reduce the max flux by 23.6% (p=0.145), and significantly reduced overall and 3-min total emission by 39.3% and 62.4%, respectively; 0.25” RO treatment significantly reduced the max flux by 63%, overall total emission by 84.7%, and total emission during 3-min agitation by 67.4%.

Soybean, maize and bean are crops of great economic importance, but in the last years suffered with infestations of the caterpillar Helicoverpa armigera, being the main problem the resistance of this pest to most pesticides. Avermectin emamectin benzoate was recently released to control this pest. Other avermectins, like abamectin, doramectin, eprinomectin and ivermectin are used in large scale because they potent acaricidal, anthelmintic, and insecticidal activities. Thus, a simple and fast method for the determination of avermectins in these crops based on a quick, easy, cheap, effective, rugged and safe (QuEChERS) extraction procedure and ultra-high performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) analysis was developed and validated. For extraction, water followed by acetonitrile:isopropanol and a partition step with salts was stablished. With the clean-up step using activated EMR-Lipid, limits of detection of 1.2 μg kg-1 for abamectin, doramectin, emamectin benzoate and ivermectin, and of 2.4 μg kg-1 for eprinomectin were achieved. Accuracy and precision evaluated at low levels presented satisfactory results. The method was successfully applied in commercial samples and is a good alternative for routine analysis.

Downwind odor characteristics can be very different depending upon the size of the upwind point-source, interim topography, and wind conditions. At one extreme, the downwind odor plume from a relatively large, confined animal feeding operation (CAFO), located on a flat open plain and under stable, near-straight-line wind conditions can be rather broad, sustained and predictable relative to a fixed receptor site downwind. In contrast, the plume from a small point-source (e.g., a roof vent stack) located on irregular topography and under rapidly shifting wind conditions can be intermittent and fleeting. These transient odor events can be surprisingly intense and offensive, in spite of their fleeting occurrence and perception. This work reports on efforts to optimize an environmental odor sampling strategy, which is optimized for the challenges of (1) sampling of such transient odor 'spikes' and (2) the prioritization of odors/odorants from multiple, closely co-located point-sources, under such transient event conditions. Protocol refinement has emerged by way of 2 environmental odor assessment projects which have been undertaken on behalf of the Missouri Department of Natural Resources. The challenge of transient odor events has been mitigated utilizing rapid, odor cued whole-air grab capture sampling into metalized-FEP gas sampling bags, followed by immediate adsorption transfer onto SPME fibers or sorbent tubes for stabilization during the shipment and storage interval between collection and final analysis. Initial results demonstrated approximately 11 fold increases in target odorant yields for 900 mL sorbent tube transfers from 2-3 second 'burst' odor event bag-captures, as compared to equivalent direct collections at the same downwind receptor location but during perceived (stable) odor 'lull' periods. Results-to-date targeting refinement and field trials of this integrated environmental odor assessment strategy are presented. Preliminary application targeting general odor sampling and point-source differentiation utilizing tracer gases is also presented.

This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via systematic survey and synthesis of related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on specific performance and users expectations as well as its technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a 3-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks.

Growing potato demands considerable external inputs of pesticides due to its susceptibility to various pests and pathogens. Here we present an attempt to differentiate the Slovak rural landscape with respect to the possibility of effective potato cultivation and to characterise soil parameters of current potato cultivation areas with the aim to increase the sustainability of the potato production. The selection was based on soil climatic, production and economic parameters. By using the GIS tools and existing databases on soil characteristics in Slovakia, maps of soil suitability categories for potato cultivation were generated. In Slovakia, it was found that 12.3% of farmland is very suitable for potato cultivation and that as much as 43.1% is not suitable. Later the specified categories were characterised in detail and specified with respect to geographic, soil, climatic, production and economic parameters. Currently, most potato crops are cultivated on Eutric Cambisols (27%), Chernozems (20%) and Mollic Fluvisols (18%). Loamy soils (65%), soils without gravel (62%), deep soils (74%) and soil situated on plains (55%) are dominant in these regions. We suggest that potato cultivation should be concentrated on the most suitable areas, thereby increasing the economic profitability, improving the ecological stability of the country and supporting the sustainability of the agriculture.

Sado Island in Niigata prefecture, Japan is one of the first Globally Important Agricultural Heritage Systems (GIAHS) among developed countries and has since been involved in environmental conservation agriculture (ECA). While ECA is still in its early stage in Japan, it has proven to be effective in mitigating climate change in the agricultural sector; hence, this study analyzed the factors which could contribute to the ECA continuation among Sado Island farmers. The data revealed the prevalence of farmers’ cognitive dissonance between ECA and its mitigating effects on climate change. Exploratory factor analysis and ordinal regression confirmed the importance of perceived GIAHS involvement in the continuation of ECA. In addition, other identified factors affecting ECA continuation fall either on a macro-level (i.e., farmers’ awareness of their role in improving their environment) or micro-level (i.e., farmers’ differing farm optimizations). These perspectives highlighted the altruistic nature of the Sado Island ECA farmers by valuing the improvement of their local and global environment as their main reason to continue ECA, whereas their various farm management optimizations support this observed farmer altruism by providing avenues to increase yield with only a moderate paddy land area. This study thereby highlights the need to continuously develop sustainable strategies to maintain and improve a positive farmer mindset towards ECA.

Climate change affects both human and natural systems. Afghanistan ranked globally on the top of highly vulnerable countries to the adverse effects of climate change. The agricultural communities of Afghanistan is highly affected by climate change. Understanding farmers’ real experiences on changing climate become crucial in planning the future adaptation strategies. This study assessed the farmers' perception of climate change and its impacts on farming communities. Primary data were collected through face-to-face interviews conducted with 120 household heads. Additional qualitative data were collected by conducting 4 Focus Group Discussions (FGDs), 4 Historical Timeline Calendars (HTCs), 18 Key Informant Interviews (KII), and sketches of 4 Crop Calendars. The study reveals that climate indicators have varied and changed. The farmers express their experiences of decreasing snowfall in winter and annual rainfall in spring and summer, which led to the intensity and frequency of drought and water shortages for agriculture and rangelands in the upper and lower part of the valleys. The temperatures in winter and summer have increased and led to earlier snow melting, earlier blooming, flowering, and greening of the plants. These changes affected both positively and negatively. There is a half-month new opportunity for cultivation and increased earlier animal ranching in the rangelands. These findings can be valuable inputs for developing effective and efficient adaptation strategies.

The risk of inhalation exposure to elevated concentrations of hydrogen sulfide (H₂S) and ammonia (NH₃) during the agitation of stored swine manure is high. Once or twice a year, farmers agitate manure before pump-out and application to fields. Agitation of the swine manure causes the short-term releases of highly toxic levels of H₂S and NH₃. In our previous pilot-scale studies, the biochar powder had shown significant mitigation of H₂S and NH₃ emissions when surficially applied to manure immediately before agitation. However, fine biochar powder application poses hazards by itself and may not be practical to apply on a farm scale, especially when livestock and workers are present. We hypothesized that applying pelletized biochar to manure surface is just as effective as applying powder to protect farmers and animals from excessive exposure to H₂S and NH₃. This work reports on the lab-scale proof-of-the-concept trials with biochar pellets on the lab-scale. The objective was to compare the biochar pellets and biochar powder on their effectiveness of mitigation on H₂S and NH₃ gases during 3-hour long swine manure agitation. Three scenarios were compared in (n=3) trials (i) control, (ii) 12.5 mm thick surficial application to manure surface of biochar powder, and (iii) an equivalent (by mass) dose of pelletized biochar applied to manure surface. The biochar powder was bound with 35% (wt) water into ~5 × 10 mm (dia × length) pellets. Biochar powder was significantly (p<0.05) more effective than the biochar pellets. Still, pellets reduced total H₂S and NH₃ emissions by ~72% and ~68%, respectively (p=0.001), compared with ~99% by powder (p=0.001). The maximum H₂S & NH₃ concentrations were reduced from 48.1±4.8 ppm & 1,810±850 ppm to 20.8±2.95 ppm & 775±182 ppm by pellets, and to 22.1±16.9 ppm & 40.3±57 ppm by powder, respectively. These reductions are equivalent to reducing the maximum concentrations of H₂S and NH₃ during the 3-h manure agitation by 57% and 57% (pellets) and 54% and 98% (powder), respectively. Treated manure properties hinted at improved nitrogen retention, yet not significant due to high variability. We recommend scaling-up and trials on the farm-scales using biochar pellets to assess the feasibility of application to large manure surfaces and techno-economic evaluation.

Climate risk is one of the confronting factors in Indian agriculture. To overcome this distrust, a large number of sensors can be installed in the fields,The extensive IoT platform can process the data sent by these sensors.The Data stream can be processed in real-timeusing Fuzzylogic, to offer smart solution. Network coding can enhance throughput and security. Thus reducing human interaction and improve efficiency.

Regionalization of food systems for shortening supply chains and developing local agriculture to feed city-regions presents particular challenges for food planning and policy. Existing foodshed approaches enable to assess the theoretical capacity of food self-sufficiency of a specific region, but they struggle to consider the diversity of existing crops in a way that could be usable for informing decisions and support urban food strategies. Most studies are based on the definition of the area required to meet local consumption, obtaining a map represented as an isotropic circle around the city, without considering the site-specific pedoclimatic, geographical and socio-economic conditions, which are essential for the development of local food supply chains. In this study we propose a first stage to fill this gap by combining the Metropolitan Foodshed and Self-sufficiency Scenario (MFSS) model, which already considers regional yields and specific land use covers, with spatially explicit data on cropping pattern, soil and topography. We use European-wide available data and apply the methodology in the city-region of Avignon (France), initially considering a foodshed with a radius of 30 Km. Our results show that even though a theoretical high potential self-sufficiency could be achieved for the whole food commodities consumed (>80%), when considering the specific pedological conditions of the area, this could be suitable only for domestic plant-based products, whereas for animal products an expansion of the initial foodshed to a radius of 100Km was required to provide >70% of self-sufficiency. We conclude that it is necessary to shift the analysis from the size assessment to the commodity-group specific spatial configuration of the foodshed based on biophysical and socio-economic features, and discuss avenues for further researches enabling to develop a foodshed assessment as a complex of complementary pieces: the foodshed archipelago.

Seven agronomic factors (crop season, farming system, harvest date, moisture, county, oat variety, and previous crop) were recorded for 202 oat crops grown across Ireland, and samples were analysed by LC-MS/MS for four major Fusarium mycotoxins: deoxynivalenol (DON), zearalenone (ZEN), T-2 toxin and HT-2 toxin. Type A trichothecenes were present in 62% of crops, with 7.4% exceeding European regulatory limits. DON (6.4%) and ZEN (9.9%) occurrences were rela-tively infrequent, though one and three samples were measured over their set limits respectively. Overall, the type of farming system and the previous crop were the main factors identified to significantly influence mycotoxin prevalence or concentration. Particularly, adherence to an organic farming system and growing oats after a previous crop of grass were found to decrease contamination by type A trichothecenes. These are important findings and may provide valuable insights for many other types of cereals crops as Europe moves towards a much greater organic based food system.

Climate risk is one of the confronting factors in Indian agriculture. To overcome this distrust, a large number of sensors can be installed in the fields,The extensive IoT platform can process the data sent by these sensors.The Data stream can be processed in real-timeusing Fuzzylogic, to offer smart solution. Network coding can enhance throughput and security. Thus reducing human interaction and improve efficiency.

rdinary people and political leaders must know the sources of greenhouse gas emissions and their effect on global climate change before they have ability to make decisions to reduce emissions and increase sinks of these gases. These people must, however, understand where greenhouse gas emissions are formed and how reductions can be made: they must understand where carbon dioxide sinks are and how to preserve or increase these sinks. North Savo is the example used in this work to describe the present emissions and sinks. There are proposals on what should and could be done to reduce greenhouse gas emissions caused by traffic, heating, forests and agriculture. There are possibilities of reducing emissions of greenhouse gases in traffic and heating in spite of the fact that the province has a low population density with long distances between homes and workplaces and schools, and a cold climate. We believe that research will also find solutions for reducing greenhouse gases and protecting waters, which are used for recreational purposes and for raw water of drinking water in many places. Luckily forests cover large areas of North Savo and their growth is an important carbon dioxide sink. In addition, forest soils serve as a valuable storage of carbon. Besides carbon dioxide emissions, emissions of nitrous oxide and methane must also be considered since they are more potent greenhouse gases than carbon dioxide and land use can thus influence these gas emissions.

The history of agricultural terraces dates back to agricultural society; and throughout history, they have enabled agricultural activities to be carried out on lands with irregular relief. They have also served as an essential element of rural landscapes due to their architectural features, building materials, and techniques, as well as the landscapes they create. Furthermore, terraces also contribute to soil conservation by preventing erosion, adding organic matter to the soil, and harvesting rainwater. The significance of agricultural terraces in the cultural landscape has triggered the need to define and classify them. In this regard, agricultural terraces across many different locations have found their place on the List of Globally Important Agricultural Heritage Sites (GIAHS) and UNESCO cultural heritage lists. In the present study, the agricultural terraces of the Erikli neighborhood of the Uzundere district were mapped through surveys, drone shots, and orthophotos, and analyzed based on the size and density indices previously used in the classification of Alpine terraces. The study is important in terms of demonstrating the use of drone technology in mapping agricultural terraces. The results of the study revealed that 51.6% of the agricultural terraces in the study area had very steep slopes and 22195 meters of total stone wall length, the average density index was 523.29 m/ha, and 76.4% of the terraces were meso and macro in character. Moreover the study shows that the terraces in the region are more susceptible to deterioration.

The agricultural industry places a high priority on crop protection, especially when it comes to protecting cotton crops against pink bollworm infestations. Rapid disease identification is essential for effective crop management at the same time. In order to overcome these difficulties, this study offers a thorough process for utilizing the Robot Operating System (ROS) to deploy an autonomous robot. The robot is intended to identify infestations of pink bollworm in addition to counting and tracking red Disease that impact cotton harvests. Drones are used to check the condition of cotton fields, and the autonomous robot's path planning component is closely related to this process. Large fields are photographed by these drones in multispectral mode, and the robot's course planning technique is done by calculating the Normalized Difference Vegetation Index (NDVI) based on these images. This approach ensures targeted surveillance and intervention, optimizing the use of resources. A customized dataset was created especially for this application to improve the robot's detecting abilities. The dataset was utilized to train a YOLOV8 model, a state-of-the-art object detection architecture. Performance characteristics of the trained model are impressive; it has a mean Average Precision (mAP) of 67.1%, Precision of 67.9%, and Recall of 61.8%. These metrics highlight how well the model works to precisely locate and measure interesting occurrences in the cotton fields. In order to address the particular challenges presented by pink bollworm infestations and crop diseases in the context of cotton cultivation, this research contributes a comprehensive solution for autonomous crop monitoring and protection by seamlessly integrating ROS, drones, NDVI calculations, and a robust detection model.

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.

Sustainable bio-economy is considered to be one of the most promising routes towards the transition to a circular and climate-neutral economy. The valorization industry of bio-waste and agri-food by-products represents a key player in bioeconomy. In this article the design and the development of a web platform aiming at promoting synergies enabling the agricultural waste valorization is presented. The platform consists of: (i) the AgriPLaCE Waste Management Database which provides the users with an extended list of potential utilization measures for a variety of agricultural waste streams (ii) the AgriPLaCE Synergies Tool which aims to create synergies between different actors involved in the value chain from agricultural waste production to waste treatment and new valuable products’ exploitation. At the initial stage, the conceptual design of both tools took place by conducting an in-depth analysis for the user needs and services alongside the system architecture. Following, the AgriPLaCE platform development stage took place, with the implementation of all the necessary subsystems. The results showed that multiple potential collaborations can arise by the use of the AgriPLaCE platform, while users can also deepen on alternative and emerging treatment technologies and valuable products that can derive from a wide range of agricultural waste streams.

Antibiotic resistance has emerged as one of the major concerns to public human health due to issues in treatment and control of major infectious diseases. From discovery of penicillin in 1940, the antibiotic resistance originated and now developed the microorganisms as resistive strains to the major available antibiotics. Furthermore, studies on this captivating activity of the microbes provide insight into the complexities of microbial physiology and may offer some guidance in preventing the onset and consequent development of antibiotic resistance. Despite of initiatives taken in last few decades to overcome the issues, the trends of antibiotic resistance gained much peak. Antibiotic resistance appears to have emerged as a result of various factors including antibiotics being misused and overused in both the medical field and the agricultural sector. Important factors in the development of antibiotic resistance also include bacterial mutation, unplanned evolution, and horizontal gene transfers. In addition, antibiotics resistance imposed the financial consequences to public health and drug development research. Future advancements related to innovative antibiotics and molecular drug designing become challenge for researchers due to intensive multiple antibiotics resistance among human population. Many researches have been conducted on origin, evolutionary aspect of antibiotic resistance and mechanisms of antibiotics resistance, but its effect on future drug development is little understood. Therefore, the recent review will highlight the role of antibiotic resistance in drug designing and impacts of antibiotic resistance on drug development in future.

In Brazil, the production of corn and soybeans has been growing significantly in recent years. Considering that the strategies for investing in the production of these crops are conditioned by production costs and risk, the objective of this article is to propose a simulation model that indicates the trend of production costs for these commodities, considering the dispersion and correlation of selected key variable prices and corn and soybean production costs practiced between the years of 2018 and 2022. Fifty specialized companies in the state of Sao Paulo, Brazil, provided data for this study, and Monte Carlo simulations showed that the total cost of corn production/ha is between USD 600.00 and USD 1150.00, with a level of certainty of 84.7%, and soybeans are in the range of USD 260.00 to USD 420.00, with a level of certainty of 86.4%. The model evidenced a trend of decreasing production costs for the crops for the 2023/24 and 2024/25 harvests, as the input with the greatest impact (fertilizer) is showing a downward trend. On the other hand, costs related to labor, soybean seed, and fungicides are showing an upward trend, while dolomitic limestone corrective remains stable.

Rice provides ~20% of human dietary energy and, for many people, a similar share of their protein. Rice cultivation, however, produces significant greenhouse gas (GHG) emissions, comparable to those from the aviation sector. The main GHG from rice production is methane, mostly a result of conventional rice cultivation (CRC) keeping rice fields continuously flooded during the crop cycle. There is extensive evidence that alternate wetting and drying (AWD) of rice fields substantially reduces methane emissions. AWD is one component of the System of Rice Intensification (SRI), an agroecological approach to the management of plants, water, soil and nutrients practiced by millions of farmers in both lowland irrigated rice and upland rainfed cultivation. Thirteen countries have included SRI in their Nationally Determined Contributions to GHG reduction or climate change mitigation. This article reviews 16 field studies of the net reduction in GHG emissions from the adoption of AWD, eight from the adoption of SRI, and two that compared SRI and AWD. Where available, the review includes data on yield and therefore on carbon dioxide-equivalent GHG emissions per kilogram of rice produced. The evidence indicates that AWD and SRI offer a similar and substantial reduction (~35–41%) in GHG emissions per hectare compared with conventional rice cultivation. However, SRI offers ~66% greater yield than CRC, and therefore greater reduction in emissions per kilogram of rice, ~54% more than AWD. The limited data directly comparing SRI and AWD support this finding. SRI also appears to have greater potential to sequester carbon in the soil. SRI lowers rice farmers’ costs of production, adds to their income and can make climate-friendly methods more attractive. Both AWD and SRI are greatly preferable to current conventional practices, but SRI offers opportunities to contribute to food security while directly addressing the drivers of climate change.

Agricultural workers utilize pesticides extensively on their farms to control weeds and insects, as well as increase crop productivity. Despite these advantages, their excessive use poses a seri-ous threat, particularly to the population living at the nexus of urban and rural areas. Exposure to pesticide drift can be investigated using geospatial tools. Remote sensing technology and Geographic Information Systems (GIS) techniques have been used intensively and constitute trusted tools in different sectors, especially in agriculture. Remote sensing depends on pro-cessing the electromagnetic radiation reflected and emitted from the ground target and can be used to identify the spectral signature of crops exposed to pesticides. GIS has powerful tools for building a spatial geo-database of pesticide exposure drift. Therefore, the major objective of the research was to explore the effectiveness of using remote sensing and GIS techniques to estimate the exposure to pesticides in Macon County (Alabama). To achieve this objective, Maximum Likelihood Classification (MLC) was used to identify accurate cropland areas. The Landsat-8 and Sentinel-2 satellite images. Available agricultural pesticide usage data (seven of the seventeen organophosphates used in Alabama) were obtained through the United States Geological Survey (USGS). The results indicated that 6.6% of Macon County’s residents are considered potentially severely exposed, and the potentially affected population resides primarily in rural areas. While 23 percent of residents of rural edges are considered to have potentially medium to high expo-sure. In addition, 38% of residents living in suburban areas are considered to have potentially low-to-medium exposure. Also, the results indicated that both GIS and remote sensing can play an effective role in estimating pesticide exposure drift.

This critical review illuminates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the dairy livestock export industry, an area facing mounting pressure for heightened efficiency and sustainability. We rigorously scrutinize the uptake of these novel technologies in identifying 'shy feeders,' automating weight monitoring of individual livestock, and refining cattle enumeration procedures. The investigation unravels their capacity to bolster animal welfare standards, minimize supply chain discrepancies, and amplify operational productivity. Moreover, the research delves into how these innovations may enhance market access and competitiveness in a swiftly shifting global dairy landscape. We further highlight the challenges encountered and future trajectories, providing a strategic framework for technology integration within the livestock export sector. Ultimately, this review underlines the importance of adopting AI and sensor technologies, indicating a shift towards precision digital livestock farming that amalgamates efficiency, animal welfare, and profitability.

Agricultural mechanization is an important factor to improve the green total factor productivity of planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of output oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sink, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The empirical analysis finds that mechanization can significantly promote the planting green total factor productivity, and this basic conclusion is still robust after using instrumental variables, sub sample regression. Further research found that the path of mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover. The mechanism of operation scale expansion, factor allocation optimization and technical efficiency change is not significant. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.

The purpose of this article is to conduct a bibliometric review of literary production in terms of market interdependence. We investigate the effect of contagion on agricultural commodities and identify the commodities and methods used in the most cited publications over the decades. For this, we used the SCOPUS database, sorting with Rayyan, Excel, and finally, the Bibliometrix/R-project. The results showed that corn, wheat, soybeans, cotton, and sugar are the most studied commodities. There is no uniqueness concerning the methods used to identify interdependence and the contagion effect. Also, it is possible to perceive the growing scientific interest concerning the subject, with a diversity of authors and magazines. However, there is a direction of the and magazines most cited articles for analyzing agricultural commodities and energy, demonstrated a worldwide trend of studies relating natural resources, biofuels, clean energy, and efficiency. In addition, countries like China and the United States stand out in this scientific scenario as the countries that produce the most articles.

This study focuses on the decisions of picking, inventory, ripening, delivering, and selling mangoes in a harvesting season. Demand, supply, and prices are uncertain, and their probability density functions are fitted based on actual trading data collected from the largest spot market in Taiwan. A stochastic programming model is formulated to minimize the expected cost under the considerations of labor, storage space, shelf life, and transportation restrictions. We implement the sample-average approximation to obtain a high-quality solution of the stochastic program. The analysis compares deterministic and stochastic solutions to assess the uncertain effect on the harvest decisions. Finally, the optimal harvest schedule of each mango type is suggested based on the stochastic program solution.

Despite offering free-of-charge COVID-19 vaccines starting July 2021, Guatemala has one of the lowest vaccination rates in Latin America. During September 28, 2021 to April 11, 2022, we conducted a cross-sectional survey of community members adapting a CDC questionnaire to evaluate COVID-19 vaccine access and hesitancy. Of 233 participants ≥12 years, 127 (55%) received >1 dose of COVID-19 and 4 (2%) reported prior COVID-19 illness. Persons ≥12 years old unvaccinated (n=106) were more likely to be female (73% vs 41%, p<0.001) and homemakers (69% vs 24%, p<0.01) compared with vaccinated participants (n=127). Among those ≥18 years, the main reported motivation for vaccination among vaccinated participants was to protect the health of family/friends (101/117, 86%); 40 (55%) unvaccinated persons reported little/no confidence in public health institutions recommending COVID-19 vaccination. Community- and/or home-based vaccination programs, including vaccination of families through the workplace, may better reach female homemakers and reduce inequities and hesitancy.

Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather forecasting framework. This paper presents an improved and fully automated solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. Main findings showed that the use of radar signal averaged over grids of a few km2 in addition to radar signal at plot scale instead of a priori weather information, provides good soil moisture estimations. The accuracy is even slightly better comparatively to the accuracy obtained using a priori weather information.

: Foodshed approaches allow the assessment of the theoretical food self-sufficiency capacity of a specific region based on biophysical conditions. Recent analyses show that the focus needs to be shifted from foodshed size portrayed as an isotropic circle to a commodity-group-specific spatial configuration of the foodshed that takes into account the socio-economic and biophysical conditions essential to the development of local food supply chains. We focus on a specific animal product (beef) and use an innovative modeling approach based on spatial analysis to detect the areas of the foodshed dedicated to beef feeding (forage, pasture, and grassland), considering the foodshed as a complex of complementary areas called an archipelago. We use available statistical data including a census to address the city-region of Avignon (France) covering a 100 km radius. Our results show that the factors driving the use of short supply chains for beef feeding areas are the foodshed archipelago’s number of patches, the connectivity between them, and the rugosity of the boundaries. In addition, our beef self-sufficiency assessment results differ depending on geographical context. For instance, being located within the perimeters of a nature park seems to help orient beef production towards short supply chains. We discuss possible leverage for public action to reconnect beef production areas to consumption areas (the city) via short supply chains (e.g. green, home-grown school food programs) so as to increase local food security through increased local food self-sufficiency.

This study analyzed the impact of climate-related natural disasters (droughts, floods, storms/rainstorms) on economic and social variables. As the Africa-specific empirical literature is limited, this study used panel data from 1961–2011 on Africa. The study used a panel data regression model analysis. The results showed that climate change-related natural disasters affected Africa’s economic growth, agriculture, and poverty and caused armed conflicts. Among the disasters, droughts are the main cause of negative impact, severely affecting crops such as maize and coffee and resulting in increased urban poverty and armed conflicts. In contrast, international aid has a positive effect but the impact is insignificant compared to the negative consequences of climate-related natural disasters. Cereal food assistance has a negative crowding-out effect on cereal production. International donors should review their interventions to support Africa’s adaptative capacity to disasters. Government efficiency has reduced the number of deaths, and this is an area that supports Africa’s adaptative efforts.

Agricultural activities profoundly affect groundwater levels in Saudi Arabia. The Al Kharj and Wadi Sahba areas, central Saudi Arabia, produce groundwater mainly from the Jurassic Arab/Jubaila aquifer and the overlying Cretaceous Biyadh aquifer. A geographic information system analysis and linear regression analyses show that from 1978 to 2016, significant agricultural overexploitation of the area’s fossil groundwater was associated with groundwater level drops of ~104 m in Al Kharj and ~48 m in Wadi Sahba. The estimated total volume of groundwater withdrawal across both aquifers during this period of wheat and alfalfa irrigation was ~27 billion m3. Landsat image analysis shows that agricultural land use decreased by ~66% over the same period due to overexploitation. The 2016 phase-out of alfalfa farming was associated with increases in groundwater levels: ~26 m in the Arab/Jubaila aquifer and ~3 m in the Biyadh aquifer. This recovery was likely due to lateral flow from surrounding fossil water within the aquifer given the absence of significant surface recharge. Model-based evaluation of surface recharge of the aquifer reveals very low potential for surface infiltration, with almost no recharge into the deep aquifer. Assuming that recovery continues at the present rate, the average groundwater level for the Arab/Jubaila aquifer is expected to reach 98 m below ground level in 2025 and 74 m below ground level in 2030.

This paper explores the coordination of the agricultural cooperative to supermarket or E-commerce sup-ply chain, under the condition of quantity loss with a mixed decay function of exponential and logistical distribution. The nature of this process is analyzed, and the corresponding demand and supply functions with single- and multi-stage discount strategies are constructed respectively to create a working model. The optimal discount ratios for supermarkets and agricultural cooperatives in decentralized and central-ized decision-making modes coupled with single- and multi-stage discounts are calculated respectively. Finally, a universal optimal strategy is designed, which can be applied to various quantity decay scenarios and makes the discount strategy more generalized. The results show that discounts can coordinate supply chains more effectively; not only is fresh agricultural produce sold before it starts to rot, but the benefit conflicts arising from both supermarkets vs. cooperatives and traditional vs. E-commerce channels are equilibrated. Further, multi-stage discounts are more effective than single-stage ones, but optimal discount ratios rely on the initial quantity of fresh agricultural produce in the supply chain; its market share in the traditional distribution channel; the potential market size; retail price; the price sensitivity coefficient of the channel; the cross-elasticity coefficient of prices between different channels; and the properties of the quantity loss.

As the global population grows, the demand for food increases and puts a strain on food production systems and agricultural productivity, causing soil degradation. Soil Quality Indexes (SQIs) have been developed to maintain and improve soil quality. However, due to the variety of soils and SQIs, analyzing and comparing results has been historically difficult. Therefore, in this study, we carried out a systematic review with meta-analysis focused on soil quality studies of agricultural soils under intensive agriculture using the unified weighted additive SQI methodology (SQIU). We analyzed 65 quality observations obtained from 22 studies. Chemical indicators were the most prevalent in the SQIUs, followed by physical and biological indicators. Conventional soil management had negative effects on soil quality (–7.55%). From the factors analyzed, the minimum database had a significant effect on the soil quality results, but not the number of indicators that made up the SQIU. The SQIU made up of chemical-biological indicators (CB) presented negatively overestimated measurements of soil quality (–32.53%), exaggerating the damage to the analyzed soils. The indicators that correlated most strongly with the size of the effect on agricultural soil quality were the cation exchange capacity (CEC), carbon to nitrogen ratio (C/N), and microbial biomass carbon (MBC). The SQIU is a feasible tool to interpret the quality of agricultural soils around the world, as it makes it possible to obtain a simple and generalized view of soil conditions.

This study evaluates the agricultural carbon emission efficiency of Shandong Province from 2011 to 2020 using the DEA-SBM model. The nexus between endogenous and exogenous variables is estimated using the Tobit model. The findings reveal an overall increasing trend in agricultural carbon emissions, with significant variations in efficiency values among different cities, leading to a severe polarization. Spatial evaluation shows a high distribution trend in the central region and low in the eastern and western regions of Shandong Province. The empirical tests conducted for Shandong Province and its three regions indicate that urbanization plays a major role in sup-porting the growth of agricultural carbon emission efficiency, while the education level of the la-bor force has a suppressive impact. Economic development and crop cultivation structure, how-ever, have no significant influence. The impact of these variables varies across the eastern, central, and western regions. The proposed countermeasures include improving planting structure and reducing brain drain in the eastern region, strengthening agricultural and rural inputs and in-creasing the added value of agricultural products in the central region, and intensifying the link-age between urbanization and industrial layout in the western region to reduce agricultural car-bon emissions efficiency in Shandong Province.

In the current situation, the world is busy with technological advances, including Indonesia. Since its arrival, many business fields have competed with each other to take part as technology users. One business sector that cannot be separated from technological support is cooperatives. On the other hand, some cooperatives in the developing phase experience technological lag. At the same time, East Kutai Regency, which is the agricultural center in East Kalimantan Province, tends to rely on the cooperative sector to encourage small and medium-scale economies. This research aims to investigate the causality between access to computers (AC), internet networks (IN), digital administration skills (DAS), and financial literacy (FL) on profits (PFT). The objectivity of the study compares agricultural cooperatives that adopt technology with adopt non-technology. Using panel data regression from eighteen sub-districts in East Kutai, it is proven that technology adopting agricultural cooperatives were more prominent than non-technology adopting agricultural cooperatives during 2017–2022. However, there is a harmony in the statistical findings from both observations, where access to computers and financial literacy both have a significant effect on profits. Other analysis results show that internet networks and digital administration skills have an insignificant impact on profits. The study's implications provide valuable output for the future sustainability of agricultural cooperatives. The success of agricultural cooperatives depends greatly on the effectiveness of the application of technology.

Sweet pepper is an important vegetable in the world. Bacterial leaf spot caused by the pathogen Xanthomonas perforans, is a limiting factor that significantly reduces the quality and yield of sweet pepper To control this disease, the use of chemical fungicides is currently the main disease control method. Thus, we want to develop an alternative method by using antagonistic microorganisms. Under this demand, Bacillus amyloliquefaciens PMB04 has strong antagonistic effects against pathogens and can inhibit the occurrence of diseases. B. amyloliquefaciens PMB04 has the potential for the development of a disease control product. Primarily, PMB04 revealed to contain a strong inhibitory effect against all isolated X. perforans strains. In the inoculation assay, the severity of bacterial spot disease on sweet peppers was reduced by PMB04 bacterial suspensions. To increase the convenience of field application for future prospects, the development of PMB04 fermentation liquid was carried out with different ratios of brown sugar and yeast extract in a 30-liter fermentation tank subsequently. Results exhibited that the fermentation liquid of 3-1 formula obtained the highest bacterial population in a 30-liter fermentation tank. The fermentation liquid of 0.5-0.5 formula was the most stable formula under two different conditions in terms of consistent bacterial population and sporulation. In addition, the 200-fold dilution of 3-1 and 0.5-0.5 fermentation liquids revealed best control efficacy on bacterial leaf spot of sweet pepper. Additionally, the results of the 0.5-0.5 fermentation liquid (PMB4FL) with different dilution concentrations also showed that the 200- and 500-fold dilutions had the best control efficacy. To understand the effect of commonly used copper-containing fungicides on sweet peppers on the application of microbial agent PMB4FL, the effects of copper hydroxide and tribasic copper sulfate on the growth of X. perforans strains and B. amyloliquefaciens PMB04 were assayed. The results exhibited that the above two fungicides did not have any inhibitory effect on the growth of PMB04, but had a strong inhibitory effect on the X. perforans strain. In the follow-up control experiment, the treatment of copper hydroxide had no synergistic effect with PMB4FL to control bacterial leaf spot. We concluded that the use of PMB4FL fermentation liquid alone on the leaves can effectively control the occurrence of bacterial leaf spot in sweet pepper crops.