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) 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 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 .

中文翻译：

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不同施氮量和气候条件下地膜玉米的蒸散量、水分利用效率和产量


可生物降解薄膜作为塑料薄膜的理想替代品，具有广阔的应用前景。然而，不同降雨季节玉米实际蒸散量（）成分、产量以及生物降解膜和地膜的水分利用效率（WUE）尚不确定。因此，进行了为期 4 年的田间试验，采用三种覆盖模式（FNM：平耕不覆盖，RPM：垄沟地膜覆盖，RBM：垄沟地膜覆盖）和两种氮肥水平（进行了 0 和 180 kg N ha)。结果表明，机器学习模型能够准确估计玉米及其划分，其中随机森林和人工神经网络模型优化后精度最高，输入变量最少。与FNM相比，旱季RBM和RPM分别增加10.8毫米、14.0毫米，平季9.1毫米、11.2毫米，雨季4.0毫米、7.5毫米，但减少75.8毫米、82.7毫米旱季分别为48.6毫米、平季56.7毫米、雨季67.1毫米、74.9毫米。因此，与FNM相比，旱季RBM和RPM分别降低65.0毫米和68.8毫米，平季分别降低39.5毫米和45.6毫米，雨季分别降低53.1毫米和67.5毫米。施氮肥也有类似的效果，但与N0相比，旱季仅增加13.3毫米，平季增加2毫米，雨季增加4.3毫米。此外，不同施氮量下的RBM和RPM在旱季分别增产4.0%、3.6%，平季增产3.0%、3.3%，雨季增产5.3%、5.9%。 WUE 旱季分别下降 23.3%、24.1%，平季分别下降 12.9%、15.0%， 21.1%，23。与 FNM 相比，雨季分别减少 4%。本研究证明，在不同降雨季节，180 kg N ha 下，RPM 可以被 RBM 替代，具有降低玉米产量、提高玉米产量、提高 WUE 的效果。本研究中优化的机器学习模型还为计算区域玉米提供了一种低成本方法。