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Agronomy
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Smart Sensing and Sustainable Crop Management Strategies for Agriculture in...
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Smart Sensing and Sustainable Crop Management Strategies for Agriculture in Arid and Semiarid Regions

A Special Issue of Agronomy (ISSN 2073-4395) belonging to the section "Farming Sustainability".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 3876

Editors

Dr. Zhenqi Liao

E-Mail Website
Guest Editor
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Interests: smart agriculture; agricultural water management; crop management; soil management
Prof. Dr. Junliang Fan

grade E-Mail Website
Guest Editor
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Interests: mechanisms and regulation of efficient water and fertilizer use in crops
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Zhu

E-Mail Website
Guest Editor
Department of Geography, The University of Hong Kong, Hong Kong SAR, China
Interests: agroecosystem modeling; climate change impacts and adaptation; environmental remote sensing; food–water–carbon nexus
Special Issues, Collections and Topics in MDPI journals
Dr. Yüze Li

E-Mail Website
Guest Editor
College of Agronomy, Sichuan Agricultural University, Chengdu 611130, China
Interests: intercropping; tillage; microbiome; rhizosphere; legumes; maize
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Arid and semiarid regions occupy more than 40% of the Earth’s land surface and sustain over one-third of the global population; however, agricultural production in these fragile ecosystems faces mounting challenges from water scarcity, soil degradation, and climate variability. Enhancing productivity while conserving natural resources is therefore critical to achieving sustainability.

Recent progress in smart sensing and digital agriculture technologies—including UAV-based remote sensing, IoT-enabled monitoring, data-driven modeling, and intelligent decision systems—has opened up new possibilities for precision and sustainable crop management. When combined with climate-resilient practices, optimized irrigation and fertilization, research into and application of slow-release/controlled-release fertilizers, conservation tillage, and diversified cropping systems, these innovations can significantly improve water- and nutrient-use efficiency, yield stability, and ecosystem resilience.

This Special Issue welcomes cutting-edge research, reviews, and case studies addressing smart sensing, sustainable agriculture practices, and crop management strategies in arid and semiarid environments. Topics include the following: precision resource management, stress detection and modeling, regenerative and conservation farming, soil–water–nutrient dynamics, adaptive root–shoot interactions, and integrated decision-support systems.

Through interdisciplinary collaboration across agronomy, remote sensing, environmental science, and intelligent technology, this Issue aims to advance high-efficiency, low-carbon, and climate-smart agriculture that ensures both productivity and sustainability in dryland regions.

Dr. Zhenqi Liao
Prof. Dr. Junliang Fan
Dr. Peng Zhu
Dr. Yüze Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart agriculture
  • sustainable agriculture practices
  • crop management strategies
  • agricultural water management
  • arid and semiarid regions

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Published Papers (5 papers)

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Research

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19 pages, 4411 KB  
Article
Spectral–Textural Feature Fusion Coupled with Machine Learning Improves UAV-Based Estimation of Maize Leaf SPAD Value in Arid Agroecosystems
by Zhenqi Liao, Lu Huang, Hui Zhang, Zhenlin Lai, Hongtai Kou, Yiyao Liu, Xiaoqian Jiang and Junliang Fan
Agronomy 2026, 16(17), 1696; https://doi.org/10.3390/agronomy16171696 - 3 Sep 2026
Viewed by 532
Abstract
Rapid and accurate monitoring of leaf nitrogen status is a key step in achieving precise fertilizer management for maize. However, UAV-based multispectral approaches relying solely on vegetation indices (VIs) may suffer from spectral saturation under dense canopies and provide limited information on canopy [...] Read more.
Rapid and accurate monitoring of leaf nitrogen status is a key step in achieving precise fertilizer management for maize. However, UAV-based multispectral approaches relying solely on vegetation indices (VIs) may suffer from spectral saturation under dense canopies and provide limited information on canopy structural variation. This study investigated whether integrating VIs and texture features (TFs) extracted from UAV multispectral imagery could improve maize SPAD estimation under different water-nitrogen management conditions and drip irrigation configurations. Time-series UAV images were collected throughout two spring maize growing seasons in a typical arid agricultural region, from which 48 VIs and 40 TFs were derived. Pearson correlation analysis combined with best subset regression was applied to identify optimal feature combinations, and five machine learning models (PLSR, ELM, RBFNN, SVM, and BPNN) were evaluated using different input feature sets. The results showed that spectral–textural feature fusion generally improved SPAD estimation performance, with validation accuracy increasing for four of the five evaluated algorithms. RBFNN achieved the highest accuracy using VIs alone (R2 = 0.807), whereas BPNN performed best when VIs and TFs were combined (R2 = 0.833). Overall, UAV-based spectral–textural fusion coupled with machine learning provides an effective approach for monitoring maize chlorophyll status and supporting precision nitrogen management in arid agricultural regions. Full article
(This article belongs to the Special Issue Smart Sensing and Sustainable Crop Management Strategies for Agriculture in Arid and Semiarid Regions)
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22 pages, 20225 KB  
Article
Unraveling the Responses of Gross Primary Productivity to Multiple Drought Types Across China Using Multi-Source Remote Sensing Data
by Liudong Zhang, Hui Xie, Hairui Li, Tao Chen and Xi Huang
Agronomy 2026, 16(14), 1361; https://doi.org/10.3390/agronomy16141361 - 17 Jul 2026
Viewed by 388
Abstract
Drought is one of the major factors affecting terrestrial ecosystem functioning under climate change. However, the interactions among different drought indices and the response of gross primary productivity (GPP) to them remain unclear. This study used multi-source remote sensing data of China from [...] Read more.
Drought is one of the major factors affecting terrestrial ecosystem functioning under climate change. However, the interactions among different drought indices and the response of gross primary productivity (GPP) to them remain unclear. This study used multi-source remote sensing data of China from 2003 to 2020, employing the standardized precipitation evapotranspiration index (SPEI), temperature condition index (TCI), soil moisture condition index (SMCI), and vegetation condition index (VCI) to characterize the spatiotemporal dynamics of drought and quantify the response mechanism between drought indices and GPP. Partial correlation analysis and structural equation modeling were used to distinguish the direct and indirect effects of different drought types on GPP. The results show that GPP exhibited a fluctuating upward trend from 2003 to 2020, ranging from 163.71 to 458.56 g C·m−2. In 2005, GPP declined significantly across most regions of China relative to 2004, decreasing by 14.47% in North China, 13.93% in Central China, 12.05% in East China, 8.45% in South China, 4.21% in Northwest China, and 4.15% in Northeast China, whereas Southwest China exhibited a slight increase of 0.34%. Different types of drought exhibited distinct occurrence characteristics. SMCI exhibited a drought frequency of 44.35% and a mean duration of 75.64 days. Among the four indices, SPEI showed the lowest frequency (12.54%) and the highest intensity (0.14), whereas TCI exhibited both the highest frequency (60.22%) and the longest duration (98.46 days), and VCI yielded the lowest drought severity (1.96). The TCI was considered a major driver of GPP variations, showing a significant positive correlation with GPP in all regions (r = 0.45–0.88, p < 0.001). In contrast, the SPEI had a relatively weak impact on GPP. The “TCI→VCI → GPP” pathway represented the main pathway of drought–GPP interaction at the national scale. This study provides methods for assessing the response characteristics of GPP under future climate change. Full article
(This article belongs to the Special Issue Smart Sensing and Sustainable Crop Management Strategies for Agriculture in Arid and Semiarid Regions)
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15 pages, 1016 KB  
Article
Stem Electrical Conductivity of Broccoli (Brassica oleracea L. var. italica Plenk) Under Nitrogen and Phosphorus Fertilizer Deficiency
by Jeong Yeon Kim, Su Kyeong Shin, Ye Eun Lee and Jin Hee Park
Agronomy 2026, 16(8), 778; https://doi.org/10.3390/agronomy16080778 - 9 Apr 2026
Cited by 2 | Viewed by 647
Abstract
Nitrogen (N) and phosphorus (P) are essential nutrients that play critical roles in plant physiological processes and the accumulation of N and P in broccoli head was significantly correlated with yield. Therefore, there is a need for a rapid, non-destructive diagnosis of crop [...] Read more.
Nitrogen (N) and phosphorus (P) are essential nutrients that play critical roles in plant physiological processes and the accumulation of N and P in broccoli head was significantly correlated with yield. Therefore, there is a need for a rapid, non-destructive diagnosis of crop status by detecting deficiencies in essential nutrients. This study evaluated the effects of N and P deficiency on field grown broccoli (Brassica oleracea L. var. italica Plenk) using a plant-induced electrical signal (PIES) sensor, in which needle electrodes are inserted into the stem to measure electrical conductivity reflecting plant water and ion status. Four treatments were established, including the control (N100P100) with sufficient N and P supply, N deficiency (N0P100), P deficiency (N100P0), and combined N–P deficiency (N0P0). For sufficient supply, urea and fused phosphate (FP) were applied at rates of 122 kg N ha−1 and 71 kg P ha−1, respectively. Soil, stem, and leaf nutrient contents, growth parameters, and stress related indicators were analyzed and their relationship with PIES values were evaluated. PIES was highest in control (N100P100) and lowest under N–P deficiency (N0P0). Higher PIES values were observed during the vegetative stage, whereas values declined during the reproductive stage, reflecting changes in physiological activity. Growth parameters such as shoot and root weight and stem diameter were generally superior in the control (N100P100) treatment, while leaf calcium (Ca), magnesium (Mg), and potassium (K) concentrations showed no significant differences among treatments. Total N content in leaves was higher in N fertilized treatments (control and P deficiency). Photosynthesis-related parameters, including soil plant analysis development (SPAD), Fv/Fm, and chlorophyll content, were lowest under N–P deficiency, which was reflected in the PIES. Principal component analysis (PCA) showed that the PIES was closely associated with growth and photosynthetic parameters and clearly distinguished N sufficient treatments (control and P deficiency) from N deficient treatments (N0P100, N0P0). Overall, these findings suggest that PIES monitoring can serve as a sensitive physiological indicator of nutrient stress and may be applied as an early diagnostic tool before visible growth inhibition occurs in broccoli cultivation. Full article
(This article belongs to the Special Issue Smart Sensing and Sustainable Crop Management Strategies for Agriculture in Arid and Semiarid Regions)
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27 pages, 3695 KB  
Article
Effects of Reduced Nitrogen Fertilization Combined with Biofertilizer Application on Cotton Growth Under Saline Water Drip Irrigation
by Xufang Lv, Shiyu Huang, Xin An, Yungang Bai, Yongbo Tong and Bangxin Ding
Agronomy 2026, 16(5), 565; https://doi.org/10.3390/agronomy16050565 - 4 Mar 2026
Cited by 1 | Viewed by 911
Abstract
Freshwater scarcity limits agricultural production in southern Xinjiang, China, while saline groundwater utilized for direct irrigation adversely affects soils and crops. Excessive nitrogen fertilizer is often applied to compensate for these adverse effects, potentially jeopardizing soil environmental quality. A two-year field experiment was [...] Read more.
Freshwater scarcity limits agricultural production in southern Xinjiang, China, while saline groundwater utilized for direct irrigation adversely affects soils and crops. Excessive nitrogen fertilizer is often applied to compensate for these adverse effects, potentially jeopardizing soil environmental quality. A two-year field experiment was conducted to assess the impact of decreased nitrogen application on cotton growth, nitrogen use efficiency, and yield under different irrigation water salinity levels, with the addition of biofertilizer. The experiment was undertaken on drip-irrigated cotton fields in southern Xinjiang, China, during 2021 and 2022. Three salinity concentrations of irrigation water were quantified: W1 (1 g L−1), W2 (3 g L−1), and W3 (7 g L−1). Under all three salinity levels, conventional fertilization (F1) served as the control, and F0, a no-nitrogen treatment, was also utilized. A total of 18 treatments were assessed using four nitrogen fertilizer application rates in conjunction with biofertilizer: no nitrogen (B0), 100% conventional nitrogen rate (B1), 85% conventional nitrogen rate (B2), and 70% conventional nitrogen rate (B3). The findings showed that adding biofertilizer considerably increased cotton output under both freshwater and brackish water irrigation regimes when compared to traditional nitrogen fertilization. In just two years, the yield of seed cotton grew by 6.15–10.56% (W1) and 6.49–11.81% (W2). In 2021, lint yield climbed by 11.79% (W1), and in two years, it increased by 6.69–15.51% (W2). Although internal nitrogen use efficiency (iNUE) initially rose and subsequently fell with escalating nitrogen rates, the application of lower nitrogen combined with biofertilizer significantly enhanced agronomic nitrogen use efficiency (aNUE) and diminished soil nitrogen residue. Recommended nitrogen application rates for cotton, utilizing 1200 kg ha−1 of biofertilizer, were established for diverse irrigation water qualities to achieve optimal nitrogen reduction, maximum iNUE, and peak yield: 283.21–322.95 kg ha−1 under freshwater irrigation (W1), 281.00–328.14 kg ha−1 under brackish water (W2) irrigation, and ≥326.28 kg ha−1 under saline irrigation (W3). These findings recommend the optimization of fertilizers across various irrigation conditions and facilitate the efficient utilization of saline water resources. Full article
(This article belongs to the Special Issue Smart Sensing and Sustainable Crop Management Strategies for Agriculture in Arid and Semiarid Regions)
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Other

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16 pages, 1004 KB  
Systematic Review
Research Advances and Emerging Challenges in Various Types of Drought Monitoring: An Integrative Review
by Haichao Yu, Sien Li, Yang Zhang, Jiaming Zhang, Jiajin Ding and Shengwen Liu
Agronomy 2026, 16(13), 1248; https://doi.org/10.3390/agronomy16131248 - 27 Jun 2026
Viewed by 405
Abstract
Drought is one of the most complex and impactful natural hazards under global climate change, exerting profound effects on water resources, agricultural productivity, ecosystem stability, and socio-economic systems. Despite extensive research, current drought studies remain fragmented due to inconsistent definitions, index-specific monitoring approaches, [...] Read more.
Drought is one of the most complex and impactful natural hazards under global climate change, exerting profound effects on water resources, agricultural productivity, ecosystem stability, and socio-economic systems. Despite extensive research, current drought studies remain fragmented due to inconsistent definitions, index-specific monitoring approaches, and limited understanding of cross-variable and cross-scale interactions. The objective of this review is to synthesize recent advances in drought monitoring and to establish an integrated understanding of drought as a coupled, multiscale process. We revisit traditional drought typologies, including meteorological, agricultural, hydrological, groundwater drought, and socio-economic drought, and critically evaluate their commonly used monitoring indices and data sources. We highlight that no single indicator can adequately capture the full dynamics of drought evolution, emphasizing the need for multi-index integration and process-based monitoring frameworks. Moreover, we examine the mechanisms of drought propagation, demonstrating that drought evolves through nonlinear and scale-dependent pathways linking atmospheric conditions, soil moisture, hydrological processes, and human water use. In particular, the emergence of flash drought reveals a shift from conventional water-deficit-driven processes to multi-process coupled dynamics, posing new challenges for early warning and prediction. Furthermore, we discuss how climate change and human activities jointly reshape drought characteristics by altering hydrological cycles, land–atmosphere interactions, and water resource management systems. The review reveals three major findings. First, drought monitoring is progressively shifting from single-index assessments toward integrated, multi-source monitoring frameworks. Second, drought propagation is inherently nonlinear and scale-dependent, involving complex interactions among climatic, hydrological, ecological, and human systems. Third, flash drought and groundwater drought have emerged as critical research frontiers due to their rapid evolution, monitoring challenges, and increasing impacts under climate change. Finally, we identify key challenges in drought research, including methodological uncertainties, data limitations, and the lack of a unified theoretical framework. These findings support a paradigm shift from traditional drought classification toward an integrated process-based perspective and provide guidance for the development of next-generation drought monitoring and early-warning systems. Full article
(This article belongs to the Special Issue Smart Sensing and Sustainable Crop Management Strategies for Agriculture in Arid and Semiarid Regions)
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