Development of a decision-making application for optimum soybean and maize fertilization strategies in Mato Grosso

Rotundo, José L.; Rech, Rafael; Cardoso, Marcelo Moraes; Fang, Yurui; Tang, Tom; Olson, Nick; Pyrik, Benjamin; Conrad, Gabe; Borrás, Lucas; Mihura, Eduardo; Messina, Carlos D.

doi:10.1016/j.compag.2021.106659

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…Moreover, the FDM constructed in this study also considers the temporal and spatial differences in crop fertilizer demands and can provide specific fertilization formulas according to the crop types and soil nutrient content of different fields. This strategy has been proven by Rotundo et al [68] to be efficient and valuable.…”

Section: Discussionmentioning

confidence: 98%

A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms

et al. 2023

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Background: The application of base fertilizer is significant for reducing agricultural costs, non-point source pollution, and increasing crop production. However, the existing fertilization decision methods require many field observations and have high prices for popularization and application. Methods: This study proposes an innovative model integrating machine learning (ML) and swarm intelligence search algorithms to overcome the above issues. Based on historical data for maize, rice, and soybean crops, ML algorithms including random forest (RF), extreme random tree (ERT), and extreme gradient boosting (XGBoost) were evaluated for predicting crop yield. Coupled with the cuckoo search algorithm (CSA), the prime fertilization decision model (FDM) was established to discover the optimal fertilization strategy. Result: For all three crops, the yield simulation accuracy of the ERT model was the highest, with an R2 and RRMSE of 0.749, 0.775, and 0.744, and 0.086, 0.051, and 0.078, respectively. Considering soil nutrient and fertilization characteristics as the determinants of yield and optimizing fertilization strategies, the proposed model can increase the average yield of maize, rice, and soybean in the study area by 23.9%, 13.3%, and 20.3%, respectively. Conclusions: The coupling model of ERT and the CSA constructed in this study can be used for the intelligent and rapid decision-making of the base fertilizer application for crops considered in the present study.

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Section: Discussionmentioning

confidence: 98%

A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms

et al. 2023

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“…As China's second-largest grain crop, increasing maize production is vital for food development [1][2][3]. Maize requires more fertilizer than other grains and legumes, and a reasonable amount of fertilizer and fertilizer application will improve the utilization efficiency of fertilizers, increase maize yields and improve maize quality [4,5]. At present, the main method of fertilizer application for maize mechanization is strip fertilization, which causes a waste of fertilizer, far from the roots, due to the large spacing between the maize plants [6].…”

Section: Introductionmentioning

confidence: 99%

Development and Numerical Simulation of a Precision Strip-Hole Layered Fertilization Subsoiler While Sowing Maize

et al. 2022

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The traditional fertilizer application methods have serious problems of environmental pollution and soil degradation due to low utilization rates in the Huang–Huai–Hai Plain of China. In this study, the conservation tillage strip-hole layered fertilization method was proposed and a precision strip-hole layered fertilizer subsoiler was designed. To meet the requirements for deep tillage strip-hole layered fertilizer application, theoretical analysis and parameter calculations were first carried out on the fertilizer application type hole wheel, and then the main factors affecting the fertilizer application effect of a strip-hole layered fertilizer shovel were analyzed. The effect of forwarding speed, angle of fertilizer tube installation (AFT) and angle of unloading fertilizer (AUF) on the middle and lower layers of the fertilizer distribution length (FDT) and fertilization amount deviation stability coefficient (FADSC) was studied using the discrete element method (DEM). The three-factor three-level full-factors test design method was adopted. Simulation results showed that the FDT and the FADSC increased as the forward speed increased; the FDT decreased as the AFT and the AUF increased; an increased FADSC was observed at a middle angle of the AFT and the AUF. The minimum FADSC was obtained for a combination of parameters with a forward speed of 2 km/h, the AFT of 35° and the AUF of 60°, corresponding to the FADSC of 2.49% in the middle layer and 2.93% in the lower layer while satisfying the FDT condition. The results of the field trials showed that the FADSC was 11.36% and 12.42%, respectively, an increase of 8.87% and 9.49%, respectively, compared to the simulation results, validating the simulation model. The new way of fertilizer application methods and a theoretical basis were provided for the design of hole application machinery.

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Efficiency analysis and evaluation of centrifugal variable-rate fertilizer spreading based on real-time spectral information on rice

Shi

Chen

XiaoChan

et al. 2023

Computers and Electronics in Agriculture

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Development of a decision-making application for optimum soybean and maize fertilization strategies in Mato Grosso

Cited by 7 publications

References 34 publications

A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms

A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms

Development and Numerical Simulation of a Precision Strip-Hole Layered Fertilization Subsoiler While Sowing Maize

Efficiency analysis and evaluation of centrifugal variable-rate fertilizer spreading based on real-time spectral information on rice

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