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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.

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

Hari Dahal

Article ID: 2401
Vol 5, Issue 1, 2024

DOI: https://doi.org/10.54517/ama.v5i1.2401
VIEWS - 3727 (Abstract)

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Abstract

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.

Keywords

spatial variability; soil nutrient distribution; SSNM; deviation square index; test value specific dose; fertilizer recommendation

References

1. Soropa G, Mbisva OM, Nyamangara J, et al. Spatial variability and mapping of soil fertility status in a high-potential smallholder farming area under sub-humid conditions in Zimbabwe. SN Applied Sciences. 2021, 3(4). doi: 10.1007/s42452-021-04367-0

2. Reza SK, Ray P, Ramachandran S, et al. Spatial Distribution of Soil Nitrogen, Phosphorus and Potassium Contents and Stocks in Humid Subtropical North-Eastern India. Journal of the Indian Society of Soil Science. 2019, 67(1): 12. doi: 10.5958/0974-0228.2019.00002.1

3. Ramzan S, Wani MA, Bhat MA. Assessment of Spatial Variability of Soil Fertility Parameters Using Geospatial Techniques in Temperate Himalayas. International Journal of Geosciences. 2017, 08(10): 1251-1263. doi: 10.4236/ijg.2017.810072

4. Yan P, Peng H, Yan L, et al. Spatial Variability of Soil Physical Properties Based on GIS and Geo-Statistical Methods in the Red Beds of the Nanxiong Basin, China. Polish Journal of Environmental Studies. 2019, 28(4): 2961-2972. doi: 10.15244/pjoes/92245

5. Zakaria Y, Shaibu AG, Baatuuwie B. Spatial analysis of soil physicochemical and hydraulic properties in the Libga irrigation scheme in northern Ghana using geostatistics and GIS approach. Soil Science Annual. 2022, 73(2): 1-8. doi: 10.37501/soilsa/149942

6. Ackerson JP. Soil Sampling Guidelines. Purdue University Extension; 2018.

7. UVM. Nutrient Recommendations for Field Crops in Vermont. The University of Vermont; 2018.

8. Warncke D, Dahl J, Jacobs L. Nutrient Recommendations for Field Crops in Michigan. Michigan State University; 2009

9. Nathan MV. Soils, Plant Nutrition and Nutrient Management. Extension, University of Missouri; 2017.

10. International Fertilizer Development Center. Nepal launches site-specific fertilizer recommendations for rice. Available online: https://ifdc.org/2022/07/25/Nepal-launches-site-specific-fertilizer-recommendations-for-rice/ (accessed on 2 June 2023).

11. Bana RC, Yadav SS, Shivran AC, et al. Site-specific Nutrient Management for Enhancing Crop Productivity. International Research Journal of Pure and Applied Chemistry. 2020, 21(15): 17-25. doi: 10.9734/irjpac/2020/v21i1530249

12. Buresh RJ. Site-specific nutrient management in rice. In: Proceedings of the International Potash Institute (IPI), and World Phosphate Institute (IMPHOS); 8 February 2007; Sharm El-Sheik, Egypt.

13. Havlin JL, Tisdale SL, Nelson WL, Beaton JD. Soil Fertility and Fertilizers an introduction to Nutrient Management, 8th ed. Pearson India Education; 2017.

14. Dinkins CP, Jones C. Interpretation of Soil Test Reports for Agriculture, Extension. Montana State University; 2013.

15. Pandit NR, Choudhary D, Maharjan S, et al. Optimum Rate and Deep Placement of Nitrogen Fertilizer Improves Nitrogen Use Efficiency and Tomato Yield in Nepal. Soil Systems. 2022, 6(3): 72. doi: 10.3390/soilsystems6030072

16. Rawal N, Pande KR, Shrestha R, et al. Soil Nutrient Balance and Soil Fertility Status under the Influence of Fertilization in Maize-Wheat Cropping System in Nepal. Applied and Environmental Soil Science. 2022, 2022: 1-11. doi: 10.1155/2022/2607468

17. Soil Management Directorate. Annual Report 2013–2014. Soil Management Directorate, Department of Agriculture, Hariharbhawan; 2014.

18. Tandan RP, Rai R, Basnet L, Karki KB. Soil nutrition distribution in Eastern Tarai of Nepal: A case study of Jhorahat VDC of Morang District. In: Proceedings of the Second National Soil Fertility Research Workshop; 24–25 March 2015; Kathmandu, Nepal.

19. Grzyb A, Wolna-Maruwka A, Niewiadomska A. Environmental Factors Affecting the Mineralization of Crop Residues. Agronomy. 2020, 10(12): 1951. doi: 10.3390/agronomy10121951

20. Dahal H. Research Perspective on Nutrient Management and Sustainability of Agriculture. Online Published Book; 2021.

21. Verma VK, Patel LB, Toor GS, Sharma PK. Spatial distribution of macronutrients in soils of arid tract of Punjab, India. International Journal of Agriculture and Biology. 2005, 7(2).

22. Tiwari KR. Soil types and fertility status in Western Terai Region of Nepal: A case from the Bankatawa VDC of the Banke District. In: Proceedings of the Second National Soil Fertality Research Workshop; 24–25 March 2015; Lalitpur, Nepal.

23. Schmidt O, Hughes-Games G. Potassium Considerations for Nutrient Management. Nutrient Management Factsheet No 7. Ministry of Agriculture and Lands; 2010.

24. Qureshi A, Singh DK, Pandey PC, et al. Site specific nutrient management approach for enhancing productivity and profitability in rice and wheat under rice-wheat cropping system. International Journal of Agricultural Sciences. 2016, 8(54): 2838–2842.

25. Verma P, Chauhan A, Ladon T. Site specific nutrient management: A review. Journal of Pharmacognosy and Phytochemistry. 2020, 9(5): 233–236.

26. Chivenge P, Zingore S, Ezui KS, et al. Progress in research on site-specific nutrient management for smallholder farmers in sub-Saharan Africa. Field Crops Research. 2022, 281: 108503. doi: 10.1016/j.fcr.2022.108503

27. Richards MB, Bahl KB, Jat ML, et al. Site Specific Nutrient Management: Implementation Guidance for Policy Makers and Investors. Global Alliance for Climate Smart Agriculture; 2015.

28. Cotteni A. Soil and Plant Testing as A Basis of Fertilizer Recommendations. FAO Soil Bulletin 38/2; 1980.

29. Sanchez PA. Properties and Management of Soils in the Tropics. John Wily and Sons; 1976.

30. Pierce FJ, Robert PC, Mangold G. Site specific management: The pros, the cons, and the realities. Available online: https://dr.lib.iastate.edu/server/api/core/bitstreams/353dabfa-f415-44c5-8049-37787740fc15/content (accessed on 20 February 2024).

31. Sida TS, Gameda S, Chamberlin J, et al. Failure to scale in digital agronomy: An analysis of site-specific nutrient management decision-support tools in developing countries. Computers and Electronics in Agriculture. 2023, 212: 108060. doi: 10.1016/j.compag.2023.108060

32. Betteridge K, Schnug E, Haneklaus. Will site specific nutrient management live up to expectation? Agriculture and Forestry Research. 2008, 2008(58): 283-294.

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Zhejiang University, China

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