Abstract
In northern China, precipitation that is primarily concentrated during the fallow period is insufficient for the growth stage, creates a moisture shortage, and leads to low, unstable yields. Yield prediction in the early growth stages significantly informs field management decisions for winter wheat (Triticum aestivum L.). A 10-year field experiment carried out in the Loess Plateau area tested how three tillage practices (deep ploughing (DP), subsoiling (SS), and no tillage (NT)) influenced cultivation and yield across different fallow periods. The experiment used the random forest (RF) algorithm to construct a prediction model of yields and yield components. Our results revealed that tillage during the fallow period was more effective than NT in improving yield in dryland wheat. Under drought condition, DP during the fallow period achieved a higher yield than SS, especially in drought years; DP was 16% higher than SS. RF was deemed fit for yield prediction across different precipitation years. An RF model was developed using meteorological factors for fixed variables and soil water storage after tillage during a fallow period for a control variable. Small error values existed in the prediction yield, spike number, and grains number per spike. Additionally, the relative error of crop yield under fallow tillage (5.24%) was smaller than that of NT (6.49%). The prediction error of relative meteorological yield was minimum and optimal, indicating that the model is suitable to explain the influence of meteorological factors on yield.
Funder
Modern Agriculture Industry Technology System Construction
National Key Research and Development Program of China
Crop Ecology and Dry Cultivation Physiology Key Laboratory of Shanxi Province
“1331” Engineering Key Innovation Cultivation Team-Organic Dry Cultivation and Cultivation Physiology Innovation Team
Subject
General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience
Reference57 articles.
1. Biomass assessment of agricultural crops using multi-temporal dual-polarimetric TerraSAR-X data;Ahmadian;PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science,2019
2. Phospred RF: prediction of protein phosphorylation sites using a consensus of Random Forest classifiers;Banerjee,2015
3. Impacts of tillage, stubble management, and nitrogen on wheat production and soil properties;Basir;Canadian Journal of Soil Science,2017
4. Principles and practices of making agriculture sustainable: crop yield prediction using Random Forest;Basha;Scalable Computing,2020
5. Random forests;Breiman;Machine Learning,2001
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献