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Featured Articles

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Weather based thumb rule models for formulating the crop insurance schemes for wheat in Punjab

Sakshi Mahajan, Prabhjyot Kaur, S. S. Sandhu

Article ID: 2522
Vol 5, Issue 1, 2024

DOI: https://doi.org/10.54517/ama.v5i1.2522
VIEWS - 90 (Abstract)

Abstract

Weather based crop insurance schemes play an important role in helping the farmers recovering their financial losses incurred due to aberrant meteorological parameters. Wheat is a major winter season crop grown in Punjab state. To formulate the weather based “weekly and monthly thumb rule models” for predicting the high yield of wheat, a study with meteorological and crop data (2007–2008 to 2021–2022) was conducted for three major wheat growing locations in the state. The results revealed that ideally the monthly maximum/minimum temperatures/rainfall/sunshine duration during the months of December, January, February and March in the range of 20–23 ℃/5–9 ℃/0–38 mm/5–8 h, 17–20 ℃/3–8 ℃/2–57 mm/4–6 h, 19–25 ℃/5–11 ℃/0–79 mm/5–8 h and 25–30 ℃/10–15 ℃/0–56 mm/8–9 h, respectively are optimum for high yield of wheat. The ideally humid (maximum/minimum relative humidity between 90%–97%/36%–63%) weather from November to February is favourable for optimum growth and development of wheat crop. Similarly, the maximum/minimum temperatures/rainfall/sunshine duration for anthesis and grain filling stage in the range of 14–23 ℃/3–10 ℃/0–55 mm/2–9 h and 18–30 ℃/5–15 ℃/0–26 mm/4–10 h are favourable for high yield of wheat crop. The maximum temperature of >18 ℃ during grain filling stage is optimum for potential yield of wheat. While the abiotic stresses like heavy rainfall, heat stress during grain filling stage are not favourable for the productivity of the crop. So, these critical limits of weather parameters are the basis for providing the weather based insurance to the farmers of the region.

Keywords

crop insurance; Punjab; rainfall; temperature; thumb rule model; wheat

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References

1. Kurukulasuriya P, Rosenthal S. Climate Change and Agriculture: A Review of impacts and adaptations. Available online: http://hdl.handle.net/10986/16616 (accessed on 8 March 2023).

2. Zobeidi T, Yazdanpanah M, Bakhshi A. Climate change risk perception among agriculture students: the role of knowledge, environmental attitude, and belief in happening. J Agr Sci Tech. 2020; 22(1): 43-55.

3. Sun Y. Enhanced Weather-Based Index Insurance Design for Hedging Crop Yield Risk. Frontiers in Plant Science. 2022; 13. doi: 10.3389/fpls.2022.895183

4. Kumar PV, Rao VUM, Bhavani O, et al. Sensitive growth stages and temperature thresholds in wheat (Triticum aestivum L.) for index-based crop insurance in the Indo-Gangetic Plains of India. The Journal of Agricultural Science. 2015; 154(2): 321-333. doi: 10.1017/s0021859615000209

5. Anonymous. Weather Based Agro Advisory Services. Available online: https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1913976 (accessed on 8 March 2023).

6. Nakka S, Jugulam M, Peterson D, et al. Herbicide resistance: Development of wheat production systems and current status of resistant weeds in wheat cropping systems. The Crop Journal. 2019; 7(6): 750-760. doi: 10.1016/j.cj.2019.09.004

7. Akter N, Rafiqul Islam M. Heat stress effects and management in wheat. A review. Agronomy for Sustainable Development. 2017; 37(5). doi: 10.1007/s13593-017-0443-9

8. Shiferaw B, Smale M, Braun HJ, et al. Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Security. 2013; 5(3): 291-317. doi: 10.1007/s12571-013-0263-y

9. Anonymous. India: wheat production volume in rabi season 2023. Available online: https://www.statista.com/statistics/1140239/india-production-volume-of wheat-during-rabi-season/ (accessed on 8 March 2023).

10. Anonymous. Package of practice of Rabi crops. Punjab Agricultural University, Ludhiana. 2023. pp. 1-21.

11. Ahmad Dar E, Brar AS, Dar SA, et al. Quantitative response of wheat to sowing dates and irrigation regimes using CERES-Wheat model. Saudi Journal of Biological Sciences. 2021; 28(11): 6198-6208. doi: 10.1016/j.sjbs.2021.06.074

12. Chenu K, Porter JR, Martre P, et al. Contribution of Crop Models to Adaptation in Wheat. Trends in Plant Science. 2017; 22(6): 472-490. doi: 10.1016/j.tplants.2017.02.003

13. Prabhjyot-Kaur, Singh H, Rao VUM, et al. Agrometeorology of wheat in Punjab state of India. Published online 2015. doi: 10.13140/RG.2.1.5105.6721

14. Gahlot S, Lin TS, Jain AK, et al. Impact of environmental changes and land management practices on wheat production in India. Earth System Dynamics. 2020; 11(3): 641-652. doi: 10.5194/esd-11-641-2020

15. Tyagi SK, Singh R, Krishnan P, et al. Variations in meteorological conditions resulted decline in wheat yield in North-West Indo-Gangetic plains. J Agric Physics. 2013; 13: 175-181.

16. Rao BB, Chowdary PS, Sandeep VM, et al. Spatial analysis of the sensitivity of wheat yields to temperature in India. Agricultural and Forest Meteorology. 2015; 200: 192-202. doi: 10.1016/j.agrformet.2014.09.023

17. Faghih H, Behmanesh J, Rezaie H, et al. Climate and rainfed wheat yield. Theoretical and Applied Climatology. 2021; 144(1-2): 13-24. doi: 10.1007/s00704-020-03478-9

18. Lee BH, Kenkel P, Brorsen BW. Pre-harvest forecasting of county wheat yield and wheat quality using weather information. Agricultural and Forest Meteorology. 2013; 168: 26-35. doi: 10.1016/j.agrformet.2012.08.010

19. Prabhjyot-Kaur, Sandhu SS, Singh S, et al. Climate Change- Punjab Scenario. Published online 2013. doi: 10.13140/RG.2.1.1368.4960

20. Prabhjyot-Kaur, Sandhu SS, Dhir A. Mitigation and risk management of climate change in crop cultivation through the adoption of Agromet Advisory Bulletin (AAB) in NICRA adopted villages in Punjab. MAUSAM. 2023; 75(1): 249-256. doi: 10.54302/mausam.v75i1.6140

21. Chattopadhyay N, Chandras S. Agrometeorological Advisory Services for Sustainable Development in Indian Agriculture. Biodiversity International Journal. 2018; 2(1). doi: 10.15406/bij.2018.02.00036

22. Vashisth A, Singh R, Das D K, et al. Weather based agromet advisories for enhancing the production and income of the farmers under changing climate scenario Int J Agric Sci Food Technol. 2013; 4: 847-50.

23. Kumar PV, Rao AVM, Chandran MA, et al. Micro-level Agromet advisory services using block level weather forecast—a new concept-based approach. Curr Sci. 2017; 112: 227–28.

24. Vishnoi L, Kumar A, Kumar S, et al. Weather based crop insurance for risk management in agriculture. J Agrometeorol 2020; 22: 101-108. doi: 10.54386/jam.v22i2.149

25. Aditya KS, Kishore A, Khan MD. Exploring farmers’ willingness to pay for crop insurance products: A case of weather-based crop insurance in Punjab, India. Agric Econ Res Rev. 2020; 33: 135-46.

26. Bala A, Prabhjyot-Kaur. Formulation of weather based “Weekly Thumb Rule Models” for prediction of potential productivity of wheat in Punjab. Int J Agric Plant Sci. 2013; 1: 17-32.

27. Gill KK, Sandhu SS, Divya, et al. Pre-harvest wheat yield prediction using CERES-wheat model for Ludhiana district, Punjab, India. Journal of Agrometeorology. 2018; 20(4): 319-321. doi: 10.54386/jam.v20i4.574

28. Sandhu SS, Kaur P, Gill KK, et al. The effect of recent climate shifts on optimal sowing windows for wheat in Punjab, India. Journal of Water and Climate Change. 2019; 11(4): 1177-1190. doi: 10.2166/wcc.2019.241

29. Sandhu SS, Prabhjyot-Kaur, Tripathu P, et al. Effect of intra-seasonal temperature on wheat at different locations of India: A study using CERES-Wheat model. Journal of Agrometeorology. 2016; 18(2): 222-233. doi: 10.54386/jam.v18i2.939

30. Praveen KM, Kamath SKV, Ranjeetha K, et al. Analyzing the impact of weather based agro-advisory services of GKMS project among arecanut growers of Udupi district of Karnataka. J Pharm Innov. 2022; 11: 07-11.

31. Kumar PV, Bal SK, Dhakar R, et al. Algorithms for weather-based management decisions in major rainfed crops of India: Validation using data from multi-location field experiments. Agron J. 2021; 113: 1816–1830. doi: 10.54386/jam.v20i4.574

32. Dupdal R, Patil BL. Economic analysis of farmer’s awareness and perception about weather based crop insurance as tool of mitigation against climate variability in north Karnataka. International Journal of Commerce and Business Management. 2017; 10(2): 77-82. doi: 10.15740/has/ijcbm/10.2/77-82

33. Dupdal R, Patil BL, Patil SL, et al. Weather based crop insurance scheme: opportunities and challenges. Int Res J Social Sci. 2020; 9: 56-59.

34. Farooq M, Bramley H, Palta JA, et al. Heat Stress in Wheat during Reproductive and Grain-Filling Phases. Critical Reviews in Plant Sciences. 2011; 30(6): 491-507. doi: 10.1080/07352689.2011.615687

35. Craufurd PQ, Vadez V, Jagadish SVK, et al. Crop science experiments designed to inform crop modeling. Agricultural and Forest Meteorology. 2013; 170: 8-18. doi: 10.1016/j.agrformet.2011.09.003

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University of the Balearic Islands Spain

News & Announcements

2023-08-22

New version of Author Guidelines are update

Please follow the journal's author guideline and the required article template to prepare your manuscript

2022-09-10

The 2022 World Food Forum will be held in Italy

The 2022 World Food Forum (WFF) flagship event will take place from 17 to 21 October at the Headquarters of the Food and Agriculture Organization of the United Nations (FAO) in Rome, Italy and virtually.

2022-06-07

Italian researchers are protesting a law that may equate “biodynamic” methods with organic agriculture

One line in the draft law concerns scientists above all. Article I states that “for the purposes of this law, the biodynamic agriculture method […], when applied in compliance with the European Union’s dispositions on organic farming, is equated with organic farming”. A few articles later, the law requires one representative of biodynamic farmers to sit in a technical committee that will oversee the application of the law, including allocations of public funds.

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Sustainability of modern agriculture

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