Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY Artificial Intelligence in Agriculture Pub Date : 2024-09-01 DOI:10.1016/j.aiia.2024.08.001
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Abstract

Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrient management, maximizing profitability, ensuring food security, and promoting environmental sustainability. We analyzed data from nutrient omission plot trials (NOPTs) conducted in 324 farmers' fields across ten agroecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields. An additive main effect and multiplicative interaction (AMMI) model was used to explain maize yield variability with nutrient addition. Interpretable machine learning (ML) algorithms in automatic machine learning (AutoML) frameworks were subsequently used to predict attainable yield relative nutrient-limited yield (RY) and to rank variables that control RY. The stack-ensemble model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model's square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature importance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn. The predicted median RY of N, P, K, and Zn, representing average soil fertility, was 0.51, 0.84, 0.87, and 0.97, accounting for 44, 54, 54, and 48% upland dry season crop area of Bangladesh, respectively. Efforts are needed to update databases cataloging variability in land type inundation classes, soil characteristics, and INS and combine them with farmers' crop management information to develop more precise nutrient guidelines for maize in the EIGP.

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营养有限的亚热带玉米产量的空间异质性预测:对印度-甘肃平原东部精确管理的影响
了解影响养分有限的亚热带玉米产量的因素以及随后的预测,对于有效进行养分管理、实现收益最大化、确保粮食安全和促进环境可持续发展至关重要。我们分析了在孟加拉国东印度-遗传平原(EIGP)10 个农业生态区(AEZ)的 324 块农田中进行的养分遗漏小区试验(NOPTs)数据,以解释玉米产量的变异性并确定控制养分限制产量的变量。采用加性主效应和乘性交互作用(AMMI)模型来解释玉米产量随养分添加量的变化。随后使用自动机器学习(AutoML)框架中的可解释机器学习(ML)算法来预测相对养分限制产量(RY)的可达到产量,并对控制 RY 的变量进行排序。在预测氮、磷和锌的可实现产量方面,堆叠-集合模型被认为是表现最好的模型。相比之下,深度学习在预测 RYK 方面的表现优于所有基础学习器。RYN、RYP、RYK 和 RYZn 的最佳模型平方误差(RMSE)分别为 0.122、0.105、0.123 和 0.104。基于置换的特征重要性技术确定土壤 pH 值是控制 RYN 和 RYP 的最关键变量。RYK 在东经方向显示较低。土壤氮和锌与 RYZn 相关。代表平均土壤肥力的氮、磷、钾和锌的预测 RY 中值分别为 0.51、0.84、0.87 和 0.97,分别占孟加拉国高地旱季作物面积的 44%、54%、54% 和 48%。需要努力更新数据库,对土地类型淹没等级、土壤特性和 INS 的变化进行编目,并将其与农民的作物管理信息相结合,以制定更精确的 EIGP 玉米养分指南。
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊最新文献
A review of external quality inspection for fruit grading using CNN models Automatic location and recognition of horse freezing brand using rotational YOLOv5 deep learning network Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains UAV-based field watermelon detection and counting using YOLOv8s with image panorama stitching and overlap partitioning Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments
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