Response of yield increase for dryland winter wheat to tillage practice during summer fallow and sowing method in the Loess Plateau of China

Li, Hui; Xue, Jian-Fu; Gao, Zhiqiang; Xue, Nai-wen; Yang, Zhenping

doi:10.1016/s2095-3119(17)61806-9

Cited by 15 publications

(9 citation statements)

References 17 publications

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“…Soil is a crucial factor affecting crop yield, including soil water and the physical and chemical properties of soil [49]. For example, soil moisture can increase winter wheat yield in dryland [50,51]. We selected 14 variables (Appendix A Table A1) that describe the physical and chemical properties of the soil.…”

Section: Soil Datamentioning

confidence: 99%

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

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Section: Soil Datamentioning

confidence: 99%

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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“…water drainage below root zone and we did not account for deep percolation. The underground water level is below five meters and drainage is thus unlikely to happen (Li et al, 2018b), consequently, drainage was assumed to be nil on the Loess Plateau (Ma et al, 2015;Zhao et al, 2004;Zhu et al, 2018), so, the equation was simplified as follows (Ma et al, 2015):…”

Section: Total Soil Water Consumption Of Wheat Growing Seasonmentioning

confidence: 99%

Optimizing the Wheat Seeding Rate for Wide-Space Sowing to Improve Yield and Water and Nitrogen Utilization

Wang

Khan

Sun

et al. 2021

Int. J. Plant Prod.

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Coordinated uptake and utilization of water and nitrogen (N) is important for high-yield and high-efficiency cultivation of winter wheat. The relationship between soil water consumption and N uptake and utilization has not previously been widely investigated. The purpose of this study was to examine the regulatory effects of different seeding rates with wide-space sowing on water consumption and N accumulation and translocation of winter wheat. A field experiment was conducted in the southeastern part of the Loess Plateau between 2016 and 2018, in which winter wheat was sown using the wide-space method (row spacing: 22-25 cm, and seedling bandwidth: 5-8 cm) and five seeding rates: 150, 225, 300, 375, and 450 kg seeds ha −1 . The results showed that soil water consumption increased significantly with an increase in the seeding rate from 150-300 kg seeds ha −1 , especially from the jointing to maturity stage of wheat growth. At 300 kg seeds ha −1 , N translocation from the leaf and stem + sheath to the grains was significantly enhanced, which increased grain N accumulation. The maximum tiller number, water use efficiency, N uptake efficiency, and partial factor productivity from applied N were all recorded at a seeding rate of 300 kg seeds ha −1 , thus, grain yield increased significantly by 6-18%. Correlation analysis showed that N accumulation from the sowing to anthesis stage, pre-anthesis N translocation, and the grain yield of winter wheat were closely related to total soil water consumption. Overall, the findings of this study have demonstrated that widespace sowing of winter wheat at a 300 kg seeds ha −1 seeding rate improved the water use efficiency and N uptake efficiency, which in turn increased the tiller number and grain yield.

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“…Several alternatives, such as the film-mulched ridge and furrow sowing method (FMR&F), with film-mulched ridges (an arc with 40 cm wide base and 10 cm height) and seeding into the furrow (rows spaced 15 cm) by using an all-in-one machine, combining ridging, mulching, fertilization, and sowing, are arising to replace the TS method. However, plastic film recycling is an issue (Liu et al, 2009;Li et al, 2018). The wide ridge and narrow furrow sowing method (WR&NF), with wide ridge (25 cm wide base and 12 cm height) and narrow furrow (depth 8 cm, sown into the top-edges of the furrow, rows spaced 12 cm) by using an allin-one machine for ridging, fertilization and sowing, is being promoted not only for conserving precipitation and decreasing soil water evaporation, but also for avoiding contamination of soil environment with plastic (Sun et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The wide ridge and narrow furrow sowing method (WR&NF), with wide ridge (25 cm wide base and 12 cm height) and narrow furrow (depth 8 cm, sown into the top-edges of the furrow, rows spaced 12 cm) by using an allin-one machine for ridging, fertilization and sowing, is being promoted not only for conserving precipitation and decreasing soil water evaporation, but also for avoiding contamination of soil environment with plastic (Sun et al, 2015). Li et al (2018) reported that various sowing methods influenced wheat yield due to changes in soil water storage and water-use efficiency on the Loess Plateau in China. However, it remained unclear how different sowing methods would influence soil bacterial diversity and abundance that contribute to the changes in soil quality and micro-environment (Mann et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Sowing Methods Influence Soil Bacterial Diversity and Community Composition in a Winter Wheat-Summer Maize Rotation System on the Loess Plateau

et al. 2020

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Response of yield increase for dryland winter wheat to tillage practice during summer fallow and sowing method in the Loess Plateau of China

Cited by 15 publications

References 17 publications

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Optimizing the Wheat Seeding Rate for Wide-Space Sowing to Improve Yield and Water and Nitrogen Utilization

Sowing Methods Influence Soil Bacterial Diversity and Community Composition in a Winter Wheat-Summer Maize Rotation System on the Loess Plateau

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