Conservation management in dryland agriculture preserves water, improves soil health and yields. To comprehend the complex interactions of conservation management and environmental factors in a rainfed forage system of the US Great Plains, distinguish the superior influence of conservation over conventional management, and have a different perspective from simulation modeling, machine learning (ML) and artificial intelligence models were adapted in 2022. The variables in this study included ten years of daily recorded weather data and yield values simulated by the DSSAT model suite, considering four years of actual data on aboveground and belowground biomass, depth-wise carbon, water content, various physicochemical soil parameters, and management practices (Sarkar and Northup 2023). Two optimized ML models, Random Forest and AdaBoost, were found to perform better, when the algorithms of six ML models- namely Decision Tree, Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost were tuned with different hyperparameters, validated and trained before predicting the biomass yields. Feature Importance plotting by these two models revealed the five most influencing similar variables, which were in different orders: average maximum temperature during daylight hours, total soil water, seasonal average minimum temperature, cumulative potential evapotranspiration and CO2. Hence, SHapley Additive exPlanation (SHAP) algorithm was adopted to dive into the database and clarify the interaction effects of management practices especially tillage and soil cover with different environmental variables. Interestingly, the SHAP model indicated soil cover as the 5th most important variable, followed by maximum temperature during daylight hours, cumulative potential evapotranspiration, seasonal minimum temperature and CO2. The interaction plotting of SHAP analysis also manifested that intensity of tillage and use of no soil cover could be detrimental. Considering the rising atmospheric CO2 levels and temperatures, along with depleting soil water, no-till practices with a springtime cover of grass peas or field peas and the addition of 100 % residue can be acclaimed for high water-use efficiency and increased aboveground biomass of rainfed sorghum sudangrass in drylands. We recommend using impeccable dataset, particularly from diverse agro-environmental systems with various tillage practices and soil covers, before regional adoption. Additionally, exploring the impacts on diverse soil types is advisable before selecting a sustainable management strategy for precision agriculture.

中文翻译：

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使用机器学习进行事前分析，以了解环境和农业管理变量对目标导向管理实践选择的交互影响


旱地农业的保护管理可以保护水分，改善土壤健康和产量。为了理解美国大平原雨养牧草系统中保护管理和环境因素的复杂相互作用，区分保护对传统管理的优越影响，并从模拟建模的角度出发，机器学习 （ML） 和人工智能模型在 2022 年进行了调整。本研究中的变量包括十年每日记录的天气数据和 DSSAT 模型套件模拟的产量值，考虑了四年的地上和地下生物量、深度碳、含水量、各种物理化学土壤参数和管理实践的实际数据（Sarkar 和 Northup 2023）。当使用不同的超参数调整六个 ML 模型（即决策树、随机森林、装袋、梯度提升、AdaBoost 和 XGBoost）的算法时，发现两个优化的 ML 模型（随机森林和 AdaBoost）表现更好，在预测生物量产量之前进行验证和训练。这两个模型的特征重要性图揭示了影响最大的 5 个相似变量，它们的顺序不同：白天的平均最高温度、土壤总水、季节平均最低温度、累积潜在蒸散和 CO2。因此，采用 SHapley 加法解释 （SHAP） 算法深入研究数据库并阐明管理实践，尤其是耕作和土壤覆盖的交互效应环境变量。 有趣的是，SHAP 模型表明土壤覆盖是第 5 大最重要的变量，其次是白天的最高温度、累积潜在蒸散、季节性最低温度和 CO2。SHAP 分析的交互作用图还表明，耕作强度和无土壤覆盖的使用可能是有害的。考虑到大气中二氧化碳水平和温度的上升，以及土壤水分的枯竭，在春季覆盖草豌豆或大田豌豆并添加 100% 残留物的免耕做法可以因高水利用效率和增加旱地雨养高粱苏丹草的地上生物量而受到赞誉。我们建议在区域采用之前使用无可挑剔的数据集，特别是来自具有不同耕作方法和土壤覆盖的不同农业环境系统的数据集。此外，在为精准农业选择可持续管理策略之前，建议探索对不同土壤类型的影响。