Submitted:
24 May 2024
Posted:
27 May 2024
You are already at the latest version
Abstract
Keywords:
1. Introduction
- -
- Innovations in sustainable agriculture: Companies, research institutions, and farmers are working on developing and implementing innovative solutions to reduce the negative environmental impact of agriculture. Technologies such as precision agriculture, smart sensor utilization, and data analysis can optimize water, fertilizer, and pesticide usage [4].
- -
- Education and societal awareness: Increasing societal awareness about the issues associated with intensive agriculture can lead to greater support for environmentally friendly farming practices. Information campaigns, educational programs, and local community actions can enhance understanding and acceptance of such practices.
- -
- -
- Continued scientific research: Continued research on the impact of agriculture on the natural environment and the quest for new methods and technologies can contribute to finding more effective solutions. Studies on plant disease resistance, resource utilization optimization, and adaptation to changing climate conditions are crucial [7].
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- International cooperation: Exchange of knowledge, experiences, and best practices among countries can accelerate progress in the field of sustainable agriculture. Initiatives such as exchange programs, international conferences, and partnerships can be effective ways to promote global cooperation [3,7,8].
2. Materials and Methods
2.1. Field Experiment
2.2. Characteristic of Potato Varieties
2.3. Observations of Potato Blight
2.4. Natural Conditions
2.5. Soil Conditions
2.6. Meteorological Conditions
2.7. Statistical Calculations
3. Results
3.1. The Rate of Spread of Potato Blight
3.2. Total and Commercial Yield of Tubers
4. Discussion
4.1. The Impact of Cultivation Systems on the Spread Rate of Potato Blight and Yield
4.2. Influence of Varieties on Potato Blight Infection and Yield
4.3. Environmental Factors and the Rate of Potato Blight Spread and Potato Yield
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- Remote sensing and GIS: The use of remote sensing and geographic information systems (GIS) enables real-time monitoring of environmental conditions favorable to the development of P. infestans. These technologies allow us to predict epidemics and take appropriate interventions [50].
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- Predictive modeling: Advanced models that take into account weather data, crop growth stages and pathogen biology can predict late blight risk, helping farmers apply fungicides more effectively and efficiently. Ibrahim et al. [2024] reported that potato blight forecast models predicted 72-96% field infection rates. The MCC model identified June’s spatiotemporal frequency of P. infestans susceptibility as a key infestation indicator. A five-day exposure period, considering temperature, precipitation, and humidity, was most effective for developing a spray system. This led to an early warning system for potato diseases in Africa’s tropical highlands, incorporating spatial hazards for a balanced approach [37].
- -
- -
- Resistant varieties: A key strategy is to grow and plant potato varieties that are resistant to P. infestans. Advances in genetic engineering and traditional breeding have led to the creation of new varieties with increased resistance [51].
- -
- -
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- Sustainable practices: Reduced chemical dependency: Integrated pest and disease management (IPM) emphasizes reduced use of chemical fungicides, relying more on biological control and resistant varieties for sustainable control of late blight [51].
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- Soil health: maintaining healthy soil through organic amendments and proper nutrient management can improve plant resistance to P. infestans [39].
4.4. Interaction of Varieties and Growing Years
4.5. Influence of Location on the Spread of Late Blight and Potato Yields
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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| Year | Composition content of the granulometric fractions [%] | Soil classification | ||||||||
| Sand | Silt | Loam | ||||||||
| 2.0-1.0 mm |
1.0-0.5 mm |
0.5-0.25 mm |
0.25-0.10 mm |
0.10-0.05 mm |
0.05-0.02 mm |
0.02-0.005 mm |
0.005-0.002 mm |
< 0.002 mm |
||
| Parczew | ||||||||||
| 2018 | 0.87 | 11.89 | 28.11 | 11.10 | 12.75 | 18.28 | 7.20 | 6.86 | 2.94 | Sandy loam |
| 2019 | 0.09 | 2.66 | 11.64 | 7.76 | 17.38 | 32.25 | 17.10 | 6.26 | 4.86 | Sand dust |
| 2020 | 1.00 | 19.92 | 32.08 | 14.00 | 8.00 | 15.00 | 6.04 | 2.96 | 1.00 | Sandy loam |
| Mean | 0.65 | 11.49 | 23.94 | 10.95 | 12.71 | 21.84 | 10.11 | 5.36 | 2,93 | Sandy loam |
| Uhnin | ||||||||||
| 2018 | 0.10 | 16.58 | 29.56 | 12.05 | 8.61 | 16.02 | 11.17 | 3.30 | 2.61 | Sandy loam |
| 2019 | 0.98 | 17.86 | 28.27 | 11.75 | 8.33 | 15.40 | 11.16 | 3.56 | 2.69 | Sandy loam |
| 2020 | 0.71 | 15.09 | 25.39 | 13.59 | 21.05 | 18.48 | 10.27 | 2.37 | 2.05 | Sandy loam |
| Mean | 0.60 | 16.51 | 27.74 | 12.50 | 12.66 | 16.63 | 10.87 | 3.08 | 2.45 | |
| Year | Content of assimilable Macronutrients [mg.100 g-1 soil] | Humus content [%] | pH [KCL] | Micronutrients content [ mg.100 kg-1 soil] | ||||||
| P2O5 | K2O | Mg | Cu | Mn | Zn | Fe | B | |||
| Parczew | ||||||||||
| 2018 | 10.5 | 11.0 | 10.4 | 1.28 | 5.95 | 1.10 | 103.23 | 2.41 | 410.04 | 0.45 |
| 2019 | 19.4 | 20.8 | 10.6 | 1.61 | 6.32 | 1.11 | 140.80 | 7.66 | 392.01 | 0.49 |
| 2020 | 22.00 | 9.0 | 11.3 | 1.63 | 6.77 | 3.14 | 114.0 | 5.77 | 846.12 | 0.47 |
| Mean | 17.15 | 13.60 | 10.77 | 1.51 | 6.35 | 1.78 | 119.34 | 5.28 | 549.99 | 0.47 |
| Uhnin | ||||||||||
| 2015 | 20.1 | 13.1 | 7.8 | 0.94 | 5.92 | 7.51 | 318 | 40.1 | 3760 | 7.24 |
| 2016 | 18.9 | 10.9 | 7.0 | 1.06 | 5.77 | 4.92 | 337 | 56.7 | 3925 | 5.28 |
| 2017 | 24.0 | 11.8 | 6.3 | 1.03 | 6.6 | 8.99 | 166 | 41.1 | 3600 | 6.04 |
| Mean | 21.0 | 11.9 | 7.03 | 1.02 | 6.09 | 7.02 | 273.8 | 45.96 | 3761.7 | 6.17 |
| Year | Month | Sum of Rainfall (mm) | Average temperature (°C) | Hydrothermal coefficient of Sielianinov* | Classification of the month according to Sielianinov with modification by Skowera [2014]. | Classification of the month according to Radomski** |
| 2018 | April | 61.8 | 8.8 | 2.3 | Wet | Dry |
| May | 120.3 | 12.8 | 3.0 | very humid | Normal | |
| June | 46.7 | 16.7 | 0.9 | Dry | very dry | |
| July | 45.2 | 19.4 | 0.8 | Dry | very dry | |
| August | 6.1 | 21.4 | 0.1 | extremely dry | Extremely dry | |
| September | 130.2 | 15.5 | 2.8 | very humid | Wet | |
| Mean 1.7 | ||||||
| 2017 | April | 47.1 | 10.0 | 1.6 | Optimum | Very dry |
| May | 46.3 | 15.3 | 1.0 | Dry | Very dry | |
| June | 87.3 | 19.1 | 1.5 | Optimum | Normal | |
| July | 114.1 | 20.5 | 1.8 | fairly humid | Normal | |
| August | 41.0 | 19.5 | 0.7 | very dry | Very dry | |
| September | 11.8 | 15.5 | 0.3 | extremely dry | Extremely dry | |
| Mean 1.2 | ||||||
| 2020 | April | 51.8 | 8.1 | 2.1 | Wet | Dry |
| May | 65.5 | 13.7 | 1.5 | Optimum | Dry | |
| June | 23.1 | 18.3 | 0.4 | extremely dry | Extremely dry | |
| July | 132.0 | 19.4 | 2.2 | Wet | Wet | |
| August | 27.0 | 20.3 | 0.4 | extremely dry | Very dry | |
| September | 83.3 | 14.8 | 1.9 | fairly humid | Normal | |
| Mean 1.4 | ||||||
| Specificalities | Years | |||
| 2018 | 2019 | 2020 | ||
| Locations | Parczew Uhnin |
0.192 0.140 |
0.201 0.165 |
0.138 0.114 |
| Cultivation systems | A* B** |
0.200 0.132 |
0.224 0.142 |
0.132 0.120 |
| Mean | 0,166 | 0.166 | 0.183 | 0.126 |
| Varieties | Resistance of potato varieties to late blight on a 9-degree scale | Crop production systems | Mean | |
| ecological | integrated | |||
| ‘Amarant’ | 6.5 | 0.146 | 0.080 | 0.113 |
| ‘Boryna’ | 5.5 | 0.079 | 0.075 | 0.077 |
| ‘Irga’ | 2.0 | 0.204 | 0.196 | 0.200 |
| ‘Jelly’ | 5.0 | 0.226 | 0.128 | 0.177 |
| ‘Jurek’ | 4.5 | 0.187 | 0.151 | 0.169 |
| ‘Mila’ | 5.0 | 0.228 | 0.132 | 0.180 |
| Varieties | Total yield | Trade yield | ||||
| Crop production systems | ||||||
| ecological | integrated | mean | Ecological | integrated | mean | |
| ‘Amarant’ | 29.1b* | 41.1b | 35.1b | 27.3b | 38.1b | 32.7b |
| ‘Boryna’ | 41.8a | 52.3a | 47.0a | 40.8a | 50.6a | 45.7a |
| ‘Irga’ | 21.4bc | 29.0c | 25.2cd | 18.6bc | 26.8c | 22.7cd |
| ‘Jelly’ | 23.1bc | 42.6b | 32.9bc | 20.1bc | 38.6b | 29.4bc |
| ‘Jurek’ | 17.4c | 32.7c | 25.1cd | 16.5d | 31.8bc | 24.1c |
| ‘Mila’ | 22.9bc | 34.0bc | 28.4c | 19.1bc | 30.5c | 24.8c |
| LSD p0.05 | 8.4 | 4.8 | 7.7 | 4.5 | ||
| Mean | 25.9b | 38.6a | 32.3 | 23.7b | 36.1a | 29.9 |
| LSD p0.05 | 1.6 | 1.5 | ||||
| Varieties | Total yield | Trade yield | ||||
| Localizations | ||||||
| Parczew | Uhnin | Mean | Parczew | Uhnin | Mean | |
| Amarant | 32.9b* | 37.2b | 35.1b | 30.7b | 34.6ab | 32.7b |
| Boryna | 43.9a | 50.1a | 47.0a | 42.1a | 49.3a | 45.7a |
| Irga | 26.9c | 23.0d | 25.0d | 24.9c | 20.5c | 22.7d |
| Jelly | 28.1bc | 37.6b | 32.9bc | 24.6c | 34.1ab | 29.4bc |
| Jurek | 19.9c | 30.3c | 25.1d | 18.0d | 30.1b | 24.1c |
| Mila | 25.7c | 31.1c | 28.4c | 22.6dc | 27.0bc | 24.8c |
| LSD p0.05 | 8.4 | 7.7 | ||||
| Mean | 25.9b | 38.6a | 32.3 | 23.7b | 36.1a | 29.9 |
| LSD p0.05 | 1.6 | 4.8 | 1.5 | 4.5 | ||
| Varieties | Total yield | Trade yield | ||||||
| Years | ||||||||
| 2018 | 2019 | 2020 | mean | 2018 | 2019 | 2020 | mean | |
| ‘Amarant’ | 34.0a* | 31.1a | 40.1b | 35.1b | 31.5a | 29.4a | 37.1b | 32.7b |
| ‘Boryna’ | 45.7a | 40.0a | 55.2a | 47.0a | 44.3a | 38.8a | 54.0a | 45.7a |
| ‘Irga’ | 30.4b | 21.7b | 23.4c | 25.2d | 27.1b | 19.8bc | 21.3c | 22.7d |
| ‘Jelly’ | 29.4b | 26.0ab | 43.2ab | 32.9bc | 26.8b | 23.3b | 38.2b | 29.4bc |
| ‘Jurek’ | 29.0b | 19.4b | 26.8c | 25.1c | 28.0b | 18.3bc | 26.2c | 24.2cd |
| ‘Mila’ | 25.4b | 24.6b | 35.1bc | 28.4c | 22.1bc | 21.0b | 31.4bc | 24.8c |
| LSD p0.05 | 14.4 | 13.5 | ||||||
| Mean | 32.3b | 27.1c | 37.3a | 32.2 | 30.0b | 25.1c | 34.7a | 29.9 |
| LSD p0.05 | 2.5 | 4.8 | 2.3 | 4.5 | ||||
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