Heng Fang , Yuannong Li , Xiaobo Gu , Yadan Du , Pengpeng Chen , Hongxiang Hu
{"title":"Evapotranspiration, water use efficiency, and yield for film mulched maize under different nitrogen-fertilization rates and climate conditions","authors":"Heng Fang , Yuannong Li , Xiaobo Gu , Yadan Du , Pengpeng Chen , Hongxiang Hu","doi":"10.1016/j.agwat.2024.108935","DOIUrl":null,"url":null,"abstract":"<div><p>The biodegradable film, as an ideal substitute for plastic film, has broad application prospects. However, it is uncertain in maize actual evapotranspiration (<span><math><msub><mrow><mi>ET</mi></mrow><mrow><mi>ac</mi></mrow></msub></math></span>) components, yield, and water use efficiency (WUE) of biodegradable and plastic films during the different rainfall seasons. Therefore, a 4-year field trial with three mulching patterns (FNM: flat planting with non-mulching, RPM: ridge-furrow with plastic film mulching, and RBM: ridge-furrow with biodegradable film mulching) and two N-fertilization levels (0 and 180 kg N ha<sup>–1</sup>) was conducted. The results showed that the machine-learning models could accurately estimate maize <span><math><msub><mrow><mi>ET</mi></mrow><mrow><mi>ac</mi></mrow></msub></math></span> and its partitioning, and the random forest and artificial neural networks models had the highest accuracy and the least input variables after optimization. Compared to FNM, RBM and RPM increased <span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> by 10.8 mm, 14.0 mm in the dry season, 9.1 mm, 11.2 mm in the normal season, and 4.0 mm, 7.5 mm in the wet season, respectively, but decreased <span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span> by 75.8 mm, 82.7 mm in the dry season, 48.6 mm, 56.7 mm in the normal season, 67.1 mm, and 74.9 mm in the wet season, respectively. Therefore, RBM and RPM decreased <span><math><msub><mrow><mi>ET</mi></mrow><mrow><mi>ac</mi></mrow></msub></math></span> by 65.0 mm, 68.8 mm in the dry season, 39.5 mm, 45.6 mm in the normal season, and 53.1 mm, 67.5 mm in the wet season, respectively, compared to FNM. Nitrogen application had a similar effect on <span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span> but only increased <span><math><msub><mrow><mi>ET</mi></mrow><mrow><mi>ac</mi></mrow></msub></math></span> by 13.3 mm in the dry season, 2 mm in the normal season, and 4.3 mm in the wet season, respectively, compared to N0. Furthermore, RBM and RPM under different nitrogen-fertilizations increased maize yield by 4.0 %, 3.6 % in the dry season, 3.0 %, 3.3 % in the normal season, and 5.3 %, 5.9 % in the wet season, respectively, also increased maize WUE by 23.3 %, 24.1 % in the dry season, 12.9 %, 15.0 % in the normal season, and 21.1 %, 23.4 % in the wet season, respectively, compared to FNM. This study proved that RPM could be replaced by RBM under 180 kg N ha<sup>–1</sup> in the different rainfall seasons in terms of reducing <span><math><msub><mrow><mi>ET</mi></mrow><mrow><mi>ac</mi></mrow></msub></math></span>, increasing maize yield, and improving WUE. The optimized machine learning models in this study also provided a low-cost method for computing regional maize <span><math><msub><mrow><mi>ET</mi></mrow><mrow><mi>ac</mi></mrow></msub></math></span>.</p></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":null,"pages":null},"PeriodicalIF":5.9000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378377424002701/pdfft?md5=57076a51047a4e127b4be6100db3a219&pid=1-s2.0-S0378377424002701-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural Water Management","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378377424002701","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
Abstract
The biodegradable film, as an ideal substitute for plastic film, has broad application prospects. However, it is uncertain in maize actual evapotranspiration () components, yield, and water use efficiency (WUE) of biodegradable and plastic films during the different rainfall seasons. Therefore, a 4-year field trial with three mulching patterns (FNM: flat planting with non-mulching, RPM: ridge-furrow with plastic film mulching, and RBM: ridge-furrow with biodegradable film mulching) and two N-fertilization levels (0 and 180 kg N ha–1) was conducted. The results showed that the machine-learning models could accurately estimate maize and its partitioning, and the random forest and artificial neural networks models had the highest accuracy and the least input variables after optimization. Compared to FNM, RBM and RPM increased by 10.8 mm, 14.0 mm in the dry season, 9.1 mm, 11.2 mm in the normal season, and 4.0 mm, 7.5 mm in the wet season, respectively, but decreased by 75.8 mm, 82.7 mm in the dry season, 48.6 mm, 56.7 mm in the normal season, 67.1 mm, and 74.9 mm in the wet season, respectively. Therefore, RBM and RPM decreased by 65.0 mm, 68.8 mm in the dry season, 39.5 mm, 45.6 mm in the normal season, and 53.1 mm, 67.5 mm in the wet season, respectively, compared to FNM. Nitrogen application had a similar effect on and but only increased by 13.3 mm in the dry season, 2 mm in the normal season, and 4.3 mm in the wet season, respectively, compared to N0. Furthermore, RBM and RPM under different nitrogen-fertilizations increased maize yield by 4.0 %, 3.6 % in the dry season, 3.0 %, 3.3 % in the normal season, and 5.3 %, 5.9 % in the wet season, respectively, also increased maize WUE by 23.3 %, 24.1 % in the dry season, 12.9 %, 15.0 % in the normal season, and 21.1 %, 23.4 % in the wet season, respectively, compared to FNM. This study proved that RPM could be replaced by RBM under 180 kg N ha–1 in the different rainfall seasons in terms of reducing , increasing maize yield, and improving WUE. The optimized machine learning models in this study also provided a low-cost method for computing regional maize .
期刊介绍:
Agricultural Water Management publishes papers of international significance relating to the science, economics, and policy of agricultural water management. In all cases, manuscripts must address implications and provide insight regarding agricultural water management.