Enhancing Maize Yield and Resource Efficiency through Controlled-Release Nitrogen Fertilization on the Semiarid Loess Plateau

Zhang, Jianjun; Zhao, Gang; Dang, Yi; Fan, Tinglu; Wang, Lei; Li, Shangzhong; Zhou, Gang; Fudjoe, Setor Kwami; Wang, Linlin; Palta, Jairo A.

doi:10.3390/agronomy13092320

Cited by 7 publications

(3 citation statements)

References 45 publications

(87 reference statements)

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“…Similarly, Wang, Y., Zhang, H., & Li, B. (2020) [8] optimised nitrogen fertilisation for maize crops in China, lowering fertiliser consumption by 20% while maintaining crop output. Furthermore, several research have looked at the effect of meteorological conditions on agricultural yields.…”

Section: Literature Surveymentioning

confidence: 99%

Revolutionizing Wheat Farming in Arid Regions With Machine-Learning Enabled Cloud Computing for Yield Simulation

2023

IRJMETS

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This research provides a thorough examination of the application of machine learning enabled cloud computing for revolutionising wheat farming in dry environments. The study aims to address the issues that farmers in these areas confront, such as erratic weather patterns, water shortages, and poor soil quality. The suggested method is creating a model that can estimate crop yields reliably based on previous weather data, soil quality, and other environmental parameters. To accomplish this aim, the study team used machine learning techniques like as neural networks to train the model on a vast collection of historical weather and yield data. The model was subsequently incorporated into a cloud computing platform, enabling for large-scale simulations and the optimisation of crop management tactics to maximise yields. The study's findings show that the recommended technique is effective in increasing agricultural yields while decreasing water use. The simulation findings show that the model can estimate yields with high accuracy, allowing farmers to make educated crop management decisions. Furthermore, the cloud computing platform allows for real-time crop monitoring, allowing for early interventions in the event of unfavourable weather events or other environmental conditions that may effect yields. Overall, the work has important implications for wheat growing in dry locations, where farmers confront unique problems. The proposed method has the potential to boost agricultural yields, minimise water use, and raise farmers' overall profitability. The study's findings may also be applied to other crops cultivated in dry environments, offering a framework for sustainable farming practises in similar settings.

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Section: Literature Surveymentioning

confidence: 99%

Revolutionizing Wheat Farming in Arid Regions With Machine-Learning Enabled Cloud Computing for Yield Simulation

2023

IRJMETS

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“…Gansu Province has been singled out as a vital hub for forage maize production, designated as a pilot area for its advancement [7]. Notably, the reported planting area for forage maize in Gansu Province reached 2.5 × 10 5 ha in 2023, underscoring its burgeoning potential for the development of livestock production [8]. Besides, forage maize also had high nutritional value, high carbohydrate content, low lignin content, and high yield per unit area, which will make the crop one of the most important cultivated forage plants in China [9].…”

Section: Introductionmentioning

confidence: 99%

Effects of Deep Vertical Rotary Tillage on Soil Water Use and Yield Formation of Forage Maize on Semiarid Land

Fang,

Tan,

Hou

et al. 2024

Agriculture

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Forage maize is one of the most important feed crops for livestock production, and is mainly grown in northwest China. However, their growth is often stressed by limited soil water availability due to the arid climate. To provide more soil moisture, a high-efficiency tillage technique was required to make crops effectively use soil moisture in deep soil layers. Deep vertical rotary tillage is a promising choice for this purpose. In this study, a long-term (2020–2022) field experiment consisting of three treatments, i.e., traditional tillage (TT), deep rotary tillage (DT), and deep vertical rotary tillage (VRT), was carried out in semiarid areas of Loess Plateau, northwest China, to investigate the effects of VRT on soil water storage (SWS), phase crop evapotranspiration (ETc) during the pre- and post-flowering periods, dry matter accumulation, grain yields and the water use efficiency (WUE) of forage maize. The results showed that VRT significantly improved the absorption of soil moisture from deep layers, especially in dry years. During the pre-flowering period of a dry year (2020), VRT decreased SWS by 7.6%–10.0% in the 60–180 cm layer, and by 17.6%–18.5% in the 180–300 cm layer, respectively, compared to DT and TT. As a result, VRT increased ETc during the pre-flowering period by 6.1% and 9.2%, respectively. In wet years (2021 and 2022), VRT increased total ETc by 2.0%–7.9% in 2021, and by 10.1%–14.9% in 2022, respectively. On average, VRT increased the dry matter weight per plant by 1.0%–7.8%, grain yields by 2.4%–38.6%, biomass yields by 3.4%–16.2%, and WUE by 10.1%–30.0%, respectively. Particularly, the benefit of VRT for increasing yields and WUE was more noticeable in dry years. It can be concluded that VRT is a drought-tolerant and yield-boosting tillage technique that is suitable for rain-fed forage maize in semiarid areas of Loess Plateau, northwest China.

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“…Numerous studies have explored the problem of N application management in maize cultivation [10][11][12]. Insufficient N application has been found to hinder maize growth and development, leading to issues such as delayed maturity [13], weakened plant vigor, and reduced leaf functionality.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Nitrogen Management for Summer Maize in the Yellow River Basin a Water Heat Carbon and N Simulator Model Approach with Entropy-Weighted Technique for Order Preference by Similarity to Ideal Solution Analysis

Wang,

Liu,

Liu

et al. 2023

Agronomy

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Summer maize constitutes a major food crop in the Yellow River Basin. Optimizing nitrogen (N) application management for this crop not only elevates its yield but also reduces N leaching, thereby ensuring food security and lessening agricultural surface pollution. Utilizing two years of summer maize field experiments, the soil water heat carbon and N simulator (WHCNS) was calibrated and validated against empirical measurements. Subsequent analyses employed the calibrated WHCNS to analyze 56 different N management scenarios. These scenarios varied in terms of N application levels, basal N to topdress application ratios, and chase ratios. The entropy-weighted TOPSIS method was utilized for the optimization, considering agronomic, environmental, and economic aspects. The model’s calibration accuracy was validated by root mean square errors, relative root mean square errors, and mean errors for soil volumetric water content and soil nitrate N content. The calibration results demonstrated that the new model was capable of simulating the soil hydraulic characteristics, N cycling, and the growth and development of summer maize during the reproductive phase in the Yellow River Basin. Scenario analyses revealed that increasing the N application initially elevated, then stabilized, summer maize yields, whereas the N agronomic efficiency first increased and then decreased. Moreover, reducing the basal N to topdress application ratios and increasing the chase ratios during the tasseling and flowering stages could minimize the nitrate N leaching and optimize both the yield and N fertilizer agronomic utilization. Specifically, the optimal N management for the current year involved applying 170 kg·ha−1 of N with a basal N to the topdress N application ratio of 1:5 and a chase ratio of 1:1 during the tasseling and flowering stages. This study lays the foundation for developing N fertilizer management strategies for summer maize cultivation in the Yellow River Basin. Furthermore, the methodology established here can be adapted for optimizing the management of diverse crops in different geographical regions.

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Enhancing Maize Yield and Resource Efficiency through Controlled-Release Nitrogen Fertilization on the Semiarid Loess Plateau

Cited by 7 publications

References 45 publications

Revolutionizing Wheat Farming in Arid Regions With Machine-Learning Enabled Cloud Computing for Yield Simulation

Revolutionizing Wheat Farming in Arid Regions With Machine-Learning Enabled Cloud Computing for Yield Simulation

Effects of Deep Vertical Rotary Tillage on Soil Water Use and Yield Formation of Forage Maize on Semiarid Land

Optimizing Nitrogen Management for Summer Maize in the Yellow River Basin a Water Heat Carbon and N Simulator Model Approach with Entropy-Weighted Technique for Order Preference by Similarity to Ideal Solution Analysis

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