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A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Crop Physiology and Crop Production".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 4100

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Special Issue Editors

Dr. Minhua Yin

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Guest Editor

College of Water Conservancy and Hydrpower Engineering, Gansu Agricultural University, Lanzhou 730070, China
Interests: water-saving irrigation; nutrient management; water-fertilizer coupling; high-efficiency production; greenhouse gas emission

Dr. Pengpeng Chen

E-Mail Website
Guest Editor

1. College of Water & Architectural Engineering, Shihezi University, Shihezi 832000, China
2. Key Laboratory of Modern Water-Saving Irrigation of Xinjiang Production & Construction Group, Shihezi University, Shihezi 832000, China
3. Key Laboratory of Northwest Oasis Water-Saving Agriculture, Ministry of Agriculture and Rural Affairs, Shihezi 832000, China
Interests: water-saving irrigation; water and fertilizer utilization; plastic mulch; residual film pollution; agricultural microplastics

Dr. Heng Fang

E-Mail Website
Guest Editor

Anhui Province Key Lab of Farmland Ecological Conservation and Nutrient Utilization, Anhui Province Engineering and Technology Research Center of Intelligent Manufacture and Efficient Utilization of Green Phosphorus Fertilizer, College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China
Interests: cereal crop; efficient utilization of water and nitrogen; green and low-carbon agronomy practices

Prof. Dr. Xiaobo Gu

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Guest Editor

Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, China
Interests: soil water; groundwater; crop modeling; data fusion
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Special Issue Information

Dear Colleagues,

Water and nutrient management play a fundamental role in ensuring sustainable agricultural production, particularly in the face of global challenges such as climate change, soil degradation, and increasing food demand. Excessive or inefficient use of water and fertilizers not only reduces crop productivity but also causes serious environmental problems, including soil salinization, groundwater contamination, and greenhouse gas emissions. Therefore, developing innovative strategies for the optimization of water and nutrient inputs has become essential in terms of promoting sustainable crop production. This Special Issue on “Water and Nutrient Management for Sustainable Crop Production” aims to provide a platform for original research and review articles that explore water-saving irrigation methods, nutrient use efficiency, soil–plant interactions, biofertilizers, precision agriculture technologies, and eco-friendly management approaches that enhance the yield and quality of crops while minimizing environmental risks.

Dr. Minhua Yin
Dr. Pengpeng Chen
Dr. Heng Fang
Prof. Dr. Xiaobo Gu
Guest Editors

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Keywords

irrigation management
fertilization strategies
water–nutrient coupling
soil–plant interactions
water use efficiency
nutrient use efficiency
sustainable crop production

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Published Papers (7 papers)

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Research

24 pages, 11110 KB

Open AccessArticle

Estimation of Nitrogen Content in Alfalfa Plants Based on Multi-Source Feature Fusion

by Jiapeng Zhu, Haohao Dang, Demin Fu, Guangping Qi, Yanxia Kang, Yanlin Ma, Siqin Zhang, Chungang Jing, Bojie Xie, Yuanbo Jiang, Jinxi Chen, Boda Li and Jun Yu

Plants 2026, 15(5), 752; https://doi.org/10.3390/plants15050752 - 28 Feb 2026

Viewed by 336

Abstract

Plant nitrogen content (PNC) is a core physiological parameter characterizing crop nitrogen nutrition status. Its precise and dynamic monitoring is crucial for crop growth diagnosis, optimizing nitrogen fertilizer management, enhancing fertilizer use efficiency, and reducing agricultural nonpoint source pollution. This study utilized multispectral imagery from unmanned aerial vehicles (UAVs) to extract vegetation indices (VIs) and texture feature values (TFVs) during critical growth stages of alfalfa. By combining TFVs to construct texture indices (TIs), variables exhibiting extremely significant correlations with alfalfa PNC (p < 0.001) were identified. We used VIs, TIs, and their combined features as model inputs. The performance of four machine learning models—random forest regression (RFR), Support Vector Regression (SVR), Backpropagation Neural Network (BPNN), and gradient boosting (XG-Boost)—was comprehensively assessed for estimating alfalfa PNC. Our results indicate the following: (1) The correlation coefficients |r| between VIs and alfalfa PNC ranged from 0.56 to 0.68; TIs constructed from TFVs significantly enhanced PNC correlation compared to raw texture values, with |r| exceeding 0.6. (2) Integrating VIs and TIs substantially improved the accuracy of PNC estimation models across growth stages. Compared to using VIs or TIs alone, the validation set R² increased by 5.4–19.7%, 1.7–16.4%, and 5.2–17.2% for the branching, budding, and initial flowering stages, respectively. (3) The XG-Boost model demonstrated optimal performance across all growth stages and input variables. Particularly during the budding stage, the VIs + TIs model achieved the highest fitting accuracy: training set R² = 0.81, RMSE = 0.15%; validation set R² = 0.80, RMSE = 0.12%. In summary, integrating multispectral vegetation indices and texture indices effectively enhances the accuracy of PNC estimation in alfalfa, providing scientific support for precision field management and fertilization decisions in alfalfa cultivation. Full article

(This article belongs to the Special Issue Water and Nutrient Management for Sustainable Crop Production)

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19 pages, 2854 KB

Open AccessArticle

Synergistic Improvement in Wheat Yield, Water and Nitrogen Use Efficiency in Wheat–Maize Rotation Systems: A Meta-Analysis of Multidimensional Agricultural Practices

by Huihui Wei, Tingting Gong, Li Zhou and Li Qin

Plants 2026, 15(4), 617; https://doi.org/10.3390/plants15040617 - 15 Feb 2026

Viewed by 541

Abstract

Agricultural practices (APs) comprehensively regulate crop growth; however, comprehensive studies evaluating the effects of APs on crop yield, water use efficiency (WUE), and nitrogen use efficiency (NUE) remain scarce, particularly regarding determining optimal APs for winter wheat in wheat–maize rotation systems. Here, this study conducted a meta-analysis based on 305 studies globally (4009 pairs of observations), focusing on five APs: irrigation, fertilization, tillage, residue utilization, and mulching. And the results indicated that APs significantly increased winter wheat yield (31.1%), NUE (14.7%), and WUE (27.6%), with fertilization showing the most pronounced effects at 43.7%, 16.9%, and 44.7%, respectively. Specifically, compared to no fertilization, combined organic and mineral fertilizer produced the highest yield increase (141.5%); among conventional fertilization, biochar addition showed the best yield increase (19.1%). Slow-controlled/-release fertilizer and inhibitor addition increased NUE by 17.7% and 26.6%, respectively, and residue utilization and mulching improved WUE (by 17.3% and 33.2%). Moreover, in cold and arid regions (mean annual temperature [MAT] < 13 °C and total annual precipitation [TAP] < 550 mm), APs showed stronger promotion of wheat yield and WUE, while in warm and humid regions, the increase in NUE was more significant (15.3–16.1%). When experiment duration was ≥5 years, APs resulted in the highest yield increase (47.9%), while NUE and WUE increased in short-term experiments. Although APs with high nitrogen application rates resulted in a greater yield increase (51.5%), fertilization significantly reduced NUE above 198 kg N ha⁻¹. Structural equation modeling revealed that, among APs, climatic conditions, soil properties, and management factors, APs were the primary driver of changes in yield and WUE, while NUE was mainly regulated by management factors. Overall, these findings provided an empirical basis for optimizing agricultural practices in wheat–maize systems and offer guidance for developing site-specific policy design. Full article

(This article belongs to the Special Issue Water and Nutrient Management for Sustainable Crop Production)

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24 pages, 6331 KB

Open AccessArticle

Study of Response of Cotton Productivity in Southern Xinjiang to Planting Patterns and Water–Nitrogen Management

by Tingbo Lv, Menghan Bian, Fulong Chen, Conghao Chen and Maoyuan Wang

Plants 2026, 15(4), 612; https://doi.org/10.3390/plants15040612 - 14 Feb 2026

Cited by 1 | Viewed by 409

Abstract

To improve cotton yield and water–nitrogen productivity in arid southern Xinjiang under climate change, field experiments conducted in 2024 (for calibration) and 2025 (for validation) were conducted in Tumushuke City to evaluate planting patterns and water–nitrogen regimes. The local conventional strategy M1T3R6 (600 mm irrigation and 825 kg N ha⁻¹) served as the control. Under the one-film–three-pipes–four-rows pattern (M1T3R4), three irrigation quotas (360, 450, and 540 mm) were combined with three N rates (495, 619, and 743 kg ha⁻¹), and the AquaCrop model was calibrated and validated. Using 40-year (1984–2023) meteorological data and SPEI-6, hydrological years were classified into four categories: wet (Y1), normal (Y2), dry (Y3), and extreme drought (Y4). Simulations assessed cotton yield (Y), water productivity (WP), and partial factor productivity of nitrogen (PFPN) under different managements, and NSGA-II with TOPSIS was used for multi-objective optimization. AquaCrop performed well for canopy cover, soil water, evapotranspiration, and yield (R² > 0.81; d > 0.85). Y, WP, and PFPN declined significantly with increasing drought severity. Compared with M1T3R6, M1T3R4 increased soil water and PFPN while reducing water and N inputs. Optimization for Y1–Y4 identified irrigation intervals of 529.9–599.1 mm and nitrogen intervals of 551.8–584.9 kg/ha, which increased yield by 8.85–21.82% while reducing irrigation by 8.33–14.15% and nitrogen by 58.6–78.1% relative to M1T3R6. Full article

(This article belongs to the Special Issue Water and Nutrient Management for Sustainable Crop Production)

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16 pages, 2836 KB

Open AccessArticle

Irrigation Depth Modulates Root Water Uptake in Subtropical Citrus Orchards: Insights from Stable Isotopes and MixSIAR Modelling

by Zhenjing Tan, Min Li, You Hu, Jinjin Zhu, Yao Peng, Sheng Deng and Zichen Jia

Plants 2026, 15(4), 537; https://doi.org/10.3390/plants15040537 - 9 Feb 2026

Viewed by 431

Abstract

Irrigation depth plays a critical role in regulating soil water availability and root water uptake in perennial orchards, yet its mechanistic effects remain poorly understood in subtropical red-soil hilly regions characterized by strong evaporative demand and shallow effective soil water storage. Here, a field experiment was conducted in a citrus orchard with three irrigation depths—shallow (25 cm), intermediate (50 cm), and deep (100 cm)—under a uniform irrigation amount. Soil water dynamics, root traits, and root water uptake sources across a 0–200 cm soil profile were investigated using soil moisture monitoring, root morphological analysis, dual stable isotopes (δ²H and δ¹⁸O), and the MixSIAR Bayesian mixing model. Irrigation depth markedly restructured vertical soil moisture patterns, with the 40–120 cm layer identified as the most responsive zone. Intermediate irrigation maintained the highest and most stable soil water content in this layer, whereas shallow irrigation intensified surface drying and deep irrigation failed to improve water availability within the hydraulically active root zone. Root surface area and dry mass were maximized under intermediate irrigation, indicating enhanced root–soil coupling. Isotopic analysis revealed the strongest evaporative fractionation under shallow irrigation, while intermediate irrigation substantially alleviated surface evaporation. MixSIAR results further showed that shallow irrigation progressively increased reliance on surface soil water (up to 93% in November), whereas intermediate irrigation promoted coordinated uptake from shallow, middle, and deep soil layers, with deep soil water contributing up to 30.7% in November. These results demonstrate that irrigation depth exerts a stronger control over root water uptake strategies by stabilizing water availability within the active root zone and reducing non-productive evaporative losses. Optimizing subsurface irrigation depth therefore represents an effective pathway to improve water-use efficiency in citrus orchards of subtropical hilly regions. Full article

(This article belongs to the Special Issue Water and Nutrient Management for Sustainable Crop Production)

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25 pages, 5911 KB

Open AccessArticle

Soil Moisture Inversion in Alfalfa via UAV with Feature Fusion and Ensemble Learning

by Jinxi Chen, Jianxin Yin, Yuanbo Jiang, Yanxia Kang, Yanlin Ma, Guangping Qi, Chungang Jin, Bojie Xie, Wenjing Yu, Yanbiao Wang, Junxian Chen, Jiapeng Zhu and Boda Li

Plants 2026, 15(3), 404; https://doi.org/10.3390/plants15030404 - 28 Jan 2026

Viewed by 333

Abstract

Timely access to soil moisture conditions in farmland crops is the foundation and key to achieving precise irrigation. Due to their high spatiotemporal resolution, unmanned aerial vehicle (UAV) remote sensing has become an important method for monitoring soil moisture. This study addresses soil moisture retrieval in alfalfa fields across different growth stages. Based on UAV multispectral images, a multi-source feature set was constructed by integrating spectral and texture features. The performance of three machine learning models—random forest regression (RFR), K-nearest neighbors regression (KNN), and XG-Boost—as well as two ensemble learning models, Voting and Stacking, was systematically compared. The results indicate the following: (1) The integrated learning models generally outperform individual machine learning models, with the Voting model performing best across all growth stages, achieving a maximum R² of 0.874 and an RMSE of 0.005; among the machine learning models, the optimal model varies with growth stage, with XG-Boost being the best during the branching and early flowering stages (maximum R² of 0.836), while RFR performs better during the budding stage (R² of 0.790). (2) The fusion of multi-source features significantly improved inversion accuracy. Taking the Voting model as an example, the accuracy of the fused features (R² = 0.874) increased by 0.065 compared to using single-texture features (R² = 0.809), and the RMSE decreased from 0.012 to 0.005. (3) In terms of inversion depth, the optimal inversion depth for the branching stage and budding stage is 40–60 cm, while the optimal depth for the early flowering stage is 20–40 cm. In summary, the method that integrates multi-source feature fusion and ensemble learning significantly improves the accuracy and stability of alfalfa soil moisture inversion, providing an effective technical approach for precise water management of artificial grasslands in arid regions. Full article

(This article belongs to the Special Issue Water and Nutrient Management for Sustainable Crop Production)

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21 pages, 3165 KB

Open AccessArticle

Response of Nitrogen Cycling in Alfalfa (Medicago sativa L.) Grassland Systems to Cropping Patterns and Nitrogen Application Rates: A Quantitative Analysis Based on Nitrogen Balance

by Yaya Duan, Jianxin Yin, Yuanbo Jiang, Haiyan Li, Wenjing Chang, Yanbiao Wang, Minhua Yin, Yanxia Kang, Yanlin Ma, Yayu Wang and Guangping Qi

Plants 2025, 14(23), 3647; https://doi.org/10.3390/plants14233647 - 29 Nov 2025

Viewed by 607

Abstract

An imbalance between the supply and demand of nutrients within the crop–soil system has resulted from the prevalent practice of excessive fertilization in agricultural agriculture. In order to increase crop growth, improve resource usage efficiency, and reduce agricultural nonpoint source pollution, appropriate cropping management techniques are essential. This study examined the effects of four nitrogen application rates (0 kg·ha⁻¹ (C0), 80 kg·ha⁻¹ (C1), 160 kg·ha⁻¹ (C2), and 240 kg·ha⁻¹ (C3)) and three alfalfa cropping systems (traditional flat planting, FP; ridge-covered biodegradable mulch, JM; and ridge-covered conventional mulch, PM) on soil inorganic nitrogen transport, nitrogen allocation within alfalfa plants, and soil N₂O emissions. Throughout the alfalfa growth phase, the dynamics of nitrogen balance within the soil–plant–atmosphere system were quantitatively examined. The findings showed: (1) The concentrations of soil NO₃⁻–N and NH₄⁺–N rose with the rate of nitrogen application but decreased with soil depth. The PMC3 treatment had the largest inorganic nitrogen reserves at the end of the alfalfa growth period. (2) The pattern of PM > JM > FP for nitrogen uptake and nitrogen accumulation in biomass in alfalfa leaves and stems peaked at the C2 nitrogen treatment rate. (3) As nitrogen application rates increased, grass-land N₂O emission flow and total emissions also followed PM > JM > FP. (4) The PMC2 treatment showed apparent nitrogen balances of 9.73 kg·ha⁻¹ and 1.84 kg·ha⁻¹ during the two-year growing season, with apparent nitrogen loss rates of 6.08% and 1.15%, respectively, both significantly lower than other treatments, according to nitrogen balance analysis. In summary, the nitrogen application pattern combining ridge-covering conventional plastic mulch with moderate nitrogen application levels can achieve nitrogen balance in alfalfa grassland systems within the Yellow River irrigation district of Gansu Province, China, and similar ecological zones. Full article

(This article belongs to the Special Issue Water and Nutrient Management for Sustainable Crop Production)

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26 pages, 10465 KB

Open AccessArticle

Water–Nitrogen Coupling Under Film Mulching Synergistically Enhances Soil Quality and Winter Wheat Yield by Restructuring Soil Microbial Co-Occurrence Networks

by Fangyuan Shen, Liangjun Fei, Youliang Peng and Yalin Gao

Plants 2025, 14(22), 3461; https://doi.org/10.3390/plants14223461 - 13 Nov 2025

Cited by 2 | Viewed by 868

Abstract

Improper irrigation and fertilization can easily lead to soil nutrient imbalance, inhibit microbial reproduction, and thereby reduce soil quality and crop yield. This study conducted winter wheat planting experiments in 2023–2025, setting three muddy water (sediment-laden irrigation water) treatments of different sediment concentrations (3, 6 and 9 kg·m⁻³), irrigation levels (0.50–0.65, 0.65–0.80 and 0.80–0.95 FC), and nitrogen application rates (100, 160 and 220 kg·ha⁻¹). An L9(3³) orthogonal experimental design was applied to evaluate the influence of water and nitrogen regulation on soil properties, microbial community structure, and wheat productivity. The results showed the following: Among these treatments, the T5 treatment (6 kg·m⁻³, 0.65–0.80 FC, 160 kg·ha⁻¹) significantly improved the root zone environment, and the total nitrogen (TN), ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and soil organic carbon (SOC) content also increased significantly. T5 also enhanced the diversity and network complexity of bacterial and fungal communities. Notably, genera such as Lysobacter, Lasiobolidium, and Ascobolus became central to nitrogen transformation and nutrient cycling. Structural equation modeling revealed the interdependent mechanism between soil quality, microorganisms, and wheat yield: NO₃⁻-N and SOC drive improvements in soil quality, while microbial community structure and network complexity are key to yield increases, with fungal communities making the largest direct contribution to yield (R² = 0.93). The T5 treatment increased two-year yields by 21.34–24.96% compared to conventional irrigation and fertilization (CK2), improved irrigation water use efficiency by 56.40–57.51% and peak nitrogen agronomic efficiency. The synergistic effect of “soil quality optimization–enhanced microbial activity–efficient utilization of water and nitrogen–high wheat yield” has been achieved, providing a theoretical basis and practical reference for scientific water and nitrogen management and sustainable yield increase in winter wheat in the Yellow River Basin and similar areas. Full article

(This article belongs to the Special Issue Water and Nutrient Management for Sustainable Crop Production)

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