Yield response and water productivity of soybean (Glycine max L.) to deficit irrigation and sowing time in south-eastern Australia

Zeleke, Ketema; Nendel, Claas

doi:10.1016/j.agwat.2024.108815

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…To meet the growing domestic demand for soybeans, the cultivation of replanting soybeans after wheat is expanding. However, water resources are limited in Xinjiang, and soybeans are highly susceptible to water deficits during the flowering and podding stage [6]. During July and August each year, Xinjiang experiences a summer drought.…”

Section: Introductionmentioning

confidence: 99%

Nitrogen Addition Mitigates Drought by Promoting Soybean (Glycine Max (Linn.) Merr) Flowering and Podding and Affecting Related Enzyme Activities

Li,

Zhang,

Liu

et al. 2024

Agriculture

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Drought at the flower and pod stage, which is the most moisture-sensitive stage of soybean development, is the main cause of yield loss in soybean. Nitrogen is a vital nutrient for soybeans. The objective of this study was to assess the potential of post-drought nitrogen fertilization at the soybean (Heihe 45) pod stage to (1) reduce pod shedding and increase yield, and (2) elucidate the mechanisms by which nitrogen fertilization regulates soybean growth under drought stress. The pot experiment was designed with two moisture levels and three nitrogen levels, resulting in a total of six treatments. The results show that nitrogen reduces cellular oxidation by regulating key enzymes of sucrose metabolism, such as sucrose synthase and sucrose phosphate synthase; and regulates cellulase to reduce shedding and mitigate drought. Comparison of low and high nitrogen conditions under drought conditions showed that the number of flowers and pods in soybean increased by 30% and 32.94%, respectively, malondialdehyde content decreased by 24%, cellulase activity in flowers and pods decreased by 15.07% and 12.31%, respectively, and yields increased by 29.98% under high nitrogen conditions. The high nitrogen treatment performed optimally and the differences between treatments reached the significant level.

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Section: Introductionmentioning

confidence: 99%

Nitrogen Addition Mitigates Drought by Promoting Soybean (Glycine Max (Linn.) Merr) Flowering and Podding and Affecting Related Enzyme Activities

Li,

Zhang,

Liu

et al. 2024

Agriculture

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Assessing the impacts of regulated deficit irrigation on soybean using AquaCrop

Barrera,

Morbidini,

Zammarchi

et al. 2024

Italian Journal of Agronomy

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Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion

Yang,

Li,

Chen

et al. 2024

Plants

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Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021–2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.

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Yield response and water productivity of soybean (Glycine max L.) to deficit irrigation and sowing time in south-eastern Australia

Cited by 3 publications

References 34 publications

Nitrogen Addition Mitigates Drought by Promoting Soybean (Glycine Max (Linn.) Merr) Flowering and Podding and Affecting Related Enzyme Activities

Nitrogen Addition Mitigates Drought by Promoting Soybean (Glycine Max (Linn.) Merr) Flowering and Podding and Affecting Related Enzyme Activities

Assessing the impacts of regulated deficit irrigation on soybean using AquaCrop

Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion

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