A sustainable approach to narrowing the summer maize yield gap experienced by smallholders in the North China Plain

Wang, Hongzhang; Ren, Hao; Zhang, Lihua; Zhao, Yali; Liu, Yuee; He, Qijin; Li, Geng; Han, Kun; Zhang, Jiwang; Zhao, Bin; Ren, Baizhao; Liu, Peng

doi:10.1016/j.agsy.2022.103541

Cited by 16 publications

(5 citation statements)

References 54 publications

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“…Irrigation and rainfed agriculture coexisted, and agricultural management measures were very complicated in the HHHP [82]. The irrigation measures will be different according to conditions and the subjective judgment of farmers [83,84], which will increase the uncertainty of crop yield prediction. The accuracy of yield prediction in irrigation and rainfed agriculture was quite different [8].…”

Section: Discussionmentioning

confidence: 99%

Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events

Zhao,

He,

Yao

et al. 2024

Remote Sensing

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The timely and robust prediction of wheat yield is very significant for grain trade and food security. In this study, the yield prediction model was developed by coupling an ensemble model with multi-source data, including vegetation indices (VIs) and meteorological data. The results showed that green chlorophyll vegetation index (GCVI) is the optimal remote sensing (RS) variable for predicting wheat yield compared with other VIs. The accuracy of the adaptive boosting- long short-term memory (AdaBoost-LSTM) ensemble model was higher than the LSTM model. AdaBoost-LSTM coupled with optimal input data had the best performance. The AdaBoost-LSTM model had strong robustness for predicting wheat yield under different irrigation and extreme weather events in general. Additionally, the accuracy of AdaBoost-LSTM for rainfed counties was higher than that for irrigation counties in most years except extreme years. The yield prediction model developed with the characteristic variables of the window from February to April had higher accuracy and smaller data requirements, which was the best prediction window. Therefore, wheat yield can be accurately predicted by the AdaBoost-LSTM model one to two months of lead time before maturity in the HHHP. Overall, the AdaBoost-LSTM model can achieve accurate and robust yield prediction in large-scale regions.

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Section: Discussionmentioning

confidence: 99%

Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events

Zhao,

He,

Yao

et al. 2024

Remote Sensing

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“…Maize (Zea mays L.) is a globally significant staple crop vital for human and animal food [1,2]. Its wide adaptability and nutritional value have led to its cultivation in diverse agroecological zones worldwide [3].…”

Section: Introductionmentioning

confidence: 99%

Molecular Diversity and Combining Ability in Newly Developed Maize Inbred Lines under Low-Nitrogen Conditions

Kamara,

Mansour,

Khalaf

et al. 2024

Life

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Nitrogen is an essential element for maize growth, but excessive application can lead to various environmental and ecological issues, including water pollution, air pollution, greenhouse gas emissions, and biodiversity loss. Hence, developing maize hybrids resilient to low-N conditions is vital for sustainable agriculture, particularly in nitrogen-deficient soils. Combining ability and genetic relationships among parental lines is crucial for breeding superior hybrids under diverse nitrogen levels. This study aimed to assess the genetic diversity of maize inbred lines using simple sequence repeat (SSR) markers and evaluate their combining ability to identify superior hybrids under low-N and recommended conditions. Local and exotic inbred lines were genotyped using SSR markers, revealing substantial genetic variation with high gene diversity (He = 0.60), moderate polymorphism information content (PIC = 0.54), and an average of 3.64 alleles per locus. Twenty-one F1 hybrids were generated through a diallel mating design using these diverse lines. These hybrids and a high yielding commercial check (SC-131) were field-tested under low-N and recommended N conditions. Significant variations (p < 0.01) were observed among nitrogen levels, hybrids, and their interaction for all recorded traits. Additive genetic variances predominated over non-additive genetic variances for grain yield and most traits. Inbred IL3 emerged as an effective combiner for developing early maturing genotypes with lower ear placement. Additionally, inbreds IL1, IL2, and IL3 showed promise as superior combiners for enhancing grain yield and related traits under both low-N and recommended conditions. Notably, hybrids IL1×IL4, IL2×IL5, IL2×IL6, and IL5×IL7 exhibited specific combining abilities for increasing grain yield and associated traits under low-N stress conditions. Furthermore, strong positive associations were identified between grain yield and specific traits like plant height, ear length, number of rows per ear, and number of kernels per row. Due to their straightforward measurability, these relationships underscore the potential of using these traits as proxies for indirect selection in early breeding generations, particularly under low-N stress. This research contributes to breeding nitrogen-efficient maize hybrids and advances our understanding of the genetic foundations for tolerance to nitrogen limitations.

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“…The North China Plain is a vital maize production hub in China, yet it faces chronic freshwater shortages [1,2]. The irregular spatial and temporal distribution of high temperatures and rainfall leads to seasonal drought stress during the growth of summer maize, resulting in lower rain-fed summer maize yields compared to irrigated crops [3][4][5]. Thus, irrigation is necessary to ensure a high yield of summer maize in this region.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Irrigation Strategy Based on Stochastic Rainfall and Evapotranspiration

Long,

Wang,

et al. 2023

Agronomy

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The North China Plain plays a pivotal role in China’s crop production, contributing to 30% of the maize yield. Nevertheless, summer maize in this region faces challenges due to climatic constraints characterized by concurrent high temperatures and rainfall during the growing season, resulting in a relatively high evapotranspiration rate. In this study, we explored eight soil moisture-based threshold irrigation strategies, consisting of two upper limits and four lower limits, along with a rainfed mode (E). The upper and lower irrigation limits are expressed as a percentage of the field’s water-holding capacity (sfc). For the four full irrigation modes (A1, A2, A3, A4), the lower limits were set at 0.6 sfc, 0.6 sfc, 0.5 sfc, and 0.5 sfc, respectively. The upper limits were defined at two levels: 0.8 sfc for A1 and A2 and sfc for A3 and A4. Similarly, for the four deficit irrigation modes (B1, B2, B3, B4), the lower limits were established at 0.4 sfc, 0.4 sfc, 0.3 sfc, and 0.3 sfc, respectively, with the upper limits set at two levels: 0.8 sfc for B1 and B2 and the full sfc for B3 and B4. To investigate the impact of rainfall and potential evapotranspiration on these irrigation modes under long-term fluctuations, we employed a stochastic framework that probabilistically linked rainfall events and irrigation applications. The Monte Carlo method was employed to simulate a long-term series (4000a) of rainfall parameters and evapotranspiration using 62 years of meteorological data from the Xinxiang region, situated in the southern part of the North China Plain. Results showed that the relative yield and net irrigation water requirement of summer maize decreased with decreasing irrigation lower limits. Additionally, the interannual variation of rainfall parameters and evapotranspiration during the growing season were remarkable, which led to the lowest relative yield of the rainfed mode (E) aligned with a larger interannual difference. According to the simulation results, mode A4 (irrigation lower limit equals 0.5 sfc, irrigation upper limit equals 0.8 sfc) could be adopted for adequate water resources. Conversely, mode B2 is more suitable for a lack of water resources.

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A sustainable approach to narrowing the summer maize yield gap experienced by smallholders in the North China Plain

Cited by 16 publications

References 54 publications

Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events

Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events

Molecular Diversity and Combining Ability in Newly Developed Maize Inbred Lines under Low-Nitrogen Conditions

Simulation of Irrigation Strategy Based on Stochastic Rainfall and Evapotranspiration

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