High-low seedbed cultivation drives the efficient utilization of key production resources and the improvement of wheat productivity in the North China Plain

Liu, Junming; Si, Zhuanyun; Wu, Lifeng; Su, Xiaojun; Gao, Yang; Duan, Aiwang

doi:10.1016/j.agwat.2023.108357

Cited by 15 publications

(11 citation statements)

References 54 publications

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“…The formula of the partial factor productivity ( PFP ) of fertilizers [ 44 , 45 ] is described as follows:

where PFP is the partial fertilizer productivity (kg kg −1 ); Y is the tomato yield (kg hm −2 ); F is the total mass of the N, P 2 O 5 , and K 2 O fertilizers (kg hm −2 ).…”

Section: Methodsmentioning

confidence: 99%

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Effects of Nutrient Solution Application Rates on Yield, Quality, and Water–Fertilizer Use Efficiency on Greenhouse Tomatoes Using Grown-in Coir

Liu,

Qiang,

Liu

et al. 2024

Plants

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The yield, quality, and water–fertilizer use efficiency of crops are important parameters for assessing rational water and fertilizer management. For an optimal water and fertilizer system with respect to the nutrient solution irrigation of greenhouse tomatoes using cultivation substrates, a two-year greenhouse cultivation experiment was conducted from 2022 to 2023. Three drip fertigation treatments (T1, T2, and T3) were implemented in the experiment, where nutrient solutions were supplied when the substrate’s water content reached 60%, 70%, and 80%. The frequency of nutrient solution applications is based on weighing coconut coir strips in the morning and evening at 7:00 to determine the daily water consumption of plants. Nutrient solutions were supplied when the substrate’s water content reached the lower limit, and the upper limit for nutrient supply was set at 100% of the substrate water content. The nutrient solution application was carried out multiple times throughout the day, avoiding the midday heat. The nutrient solution formula used was the soilless tomato cultivation formula from South China Agricultural University. The results show that plant height and the leaf area index rapidly increased in the early and middle stages, and later growth tended to stabilize; the daily transpiration of tomatoes increased with an increase in nutrient solution supply, and it was the greatest in the T3 treatment. Between the amount of nutrient solution application and the number of years, the yield increased with the increase of the amount of nutrient solution, showing T3 > T2 > T1. Although the average yield of the T2 treatment was slightly lower than that of the T3 treatment by 3.65%, the average irrigation water use efficiency, water use efficiency, and partial fertilizer productivity of the T2 treatment were significantly higher than those of the T3 treatment by 29.10%, 19.99%, and 28.89%, respectively (p < 0.05). Additionally, soluble solid, vitamin C, and soluble sugar contents and the sugar–acid ratio of tomatoes in the T2 treatment were greater than those in the other two treatments (p < 0.05). Using the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method, it was concluded that the nutrient solution application rate of 70% can significantly increase water and fertilizer use efficiency and markedly improve the nutritional and flavor quality of the fruit without a significant reduction in yield. This finding provides significant guidance for the high-yield, high-quality, and efficient production of coconut coir-based cultivated tomatoes in greenhouses.

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“…The formula of the partial factor productivity ( PFP ) of fertilizers [ 44 , 45 ] is described as follows:

where PFP is the partial fertilizer productivity (kg kg −1 ); Y is the tomato yield (kg hm −2 ); F is the total mass of the N, P 2 O 5 , and K 2 O fertilizers (kg hm −2 ).…”

Section: Methodsmentioning

confidence: 99%

“…where WUE is water use efficiency (kg m −3 ); Y a is the tomato yield (kg plant −1 ); ET is the total water consumption of tomato during the whole growth period (m 3 plant −1 ). The formula of the partial factor productivity (PFP) of fertilizers [44,45] is described as follows:…”

Section: Tomato Yield Irrigation Use Efficiency Water Use Efficiency ...mentioning

confidence: 99%

Effects of Nutrient Solution Application Rates on Yield, Quality, and Water–Fertilizer Use Efficiency on Greenhouse Tomatoes Using Grown-in Coir

Liu,

Qiang,

Liu

et al. 2024

Plants

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“…Total nitrogen content was determined by the Kjeldahl method [31]. The day after the drone flew at each fertility stage, the leaves were collected, 60 sampling points were randomly selected in the experimental area, and 90 sampling sites were selected in the pilot area in 2023.…”

Section: Total Nitrogen Content Of Apple Leavesmentioning

confidence: 99%

An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods

Zhao,

Zhao

et al. 2024

Agronomy

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Accurate nitrogen fertilizer management determines the yield and quality of fruit trees, but there is a lack of multispectral UAV-based nitrogen fertilizer monitoring technology for orchards. Therefore, in this study, a field experiment was conducted by UAV to acquire multispectral images of an apple orchard with dwarf stocks and dense planting in southern Xinjiang and to estimate the nitrogen content of canopy leaves of apple trees by using three machine learning methods. The three inversion methods were partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RFR). The results showed that the RF model could significantly improve the accuracy of estimating the leaf nitrogen content of the apple tree canopy, and the validation set of the four periods of apple trees ranged from 0.670 to 0.797 for R2, 0.838 mg L−1 to 4.403 mg L−1 for RMSE, and 1.74 to 2.222 for RPD, among which the RF model of the pre-fruit expansion stage of the 2023 season had the highest accuracy. This paper shows that the apple tree leaf nitrogen content estimation model based on multispectral UAV images constructed by using the RF machine learning method can timely and accurately diagnose the growth condition of apple trees, provide technical support for precise nitrogen fertilizer management in orchards, and provide a certain scientific basis for tree crop growth.

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“…Winter wheat holds tremendous significance in agriculture as one of the most widely cultivated and consumed food crops across the globe. It plays a pivotal role in providing a substantial portion of food and energy to the world’s population and constitutes an integral part of people’s daily diets ( Liu et al., 2023 ). The production of winter wheat also carries great importance for a nation’s food supply and the overall food security of its citizens ( Li et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

An integrated feature selection approach to high water stress yield prediction

Li,

Zhou,

Cheng

et al. 2023

Front. Plant Sci.

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The timely and precise prediction of winter wheat yield plays a critical role in understanding food supply dynamics and ensuring global food security. In recent years, the application of unmanned aerial remote sensing has significantly advanced agricultural yield prediction research. This has led to the emergence of numerous vegetation indices that are sensitive to yield variations. However, not all of these vegetation indices are universally suitable for predicting yields across different environments and crop types. Consequently, the process of feature selection for vegetation index sets becomes essential to enhance the performance of yield prediction models. This study aims to develop an integrated feature selection method known as PCRF-RFE, with a focus on vegetation index feature selection. Initially, building upon prior research, we acquired multispectral images during the flowering and grain filling stages and identified 35 yield-sensitive multispectral indices. We then applied the Pearson correlation coefficient (PC) and random forest importance (RF) methods to select relevant features for the vegetation index set. Feature filtering thresholds were set at 0.53 and 1.9 for the respective methods. The union set of features selected by both methods was used for recursive feature elimination (RFE), ultimately yielding the optimal subset of features for constructing Cubist and Recurrent Neural Network (RNN) yield prediction models. The results of this study demonstrate that the Cubist model, constructed using the optimal subset of features obtained through the integrated feature selection method (PCRF-RFE), consistently outperformed the RNN model. It exhibited the highest accuracy during both the flowering and grain filling stages, surpassing models constructed using all features or subsets derived from a single feature selection method. This confirms the efficacy of the PCRF-RFE method and offers valuable insights and references for future research in the realms of feature selection and yield prediction studies.

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High-low seedbed cultivation drives the efficient utilization of key production resources and the improvement of wheat productivity in the North China Plain

Cited by 15 publications

References 54 publications

Effects of Nutrient Solution Application Rates on Yield, Quality, and Water–Fertilizer Use Efficiency on Greenhouse Tomatoes Using Grown-in Coir

Effects of Nutrient Solution Application Rates on Yield, Quality, and Water–Fertilizer Use Efficiency on Greenhouse Tomatoes Using Grown-in Coir

An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods

An integrated feature selection approach to high water stress yield prediction

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