Agriculture

27 pages, 1902 KB

Open AccessReview

Improving Crop Resilience in Drought-Prone Agroecosystems: Bioinoculants and Biocontrol Strategies from Climate-Adaptive Microorganisms

by Dulanjalee L. Harishchandra, Anuruddha Karunarathna, Sukanya Haituk, Sirikanlaya Sittihan, Thitima Wongwan and Ratchadawan Cheewangkoon

Agriculture 2025, 15(23), 2479; https://doi.org/10.3390/agriculture15232479 (registering DOI) - 28 Nov 2025

Abstract

Agricultural production is becoming increasingly difficult due to various environmental fluctuations brought on by climate change. Overall increase in atmospheric temperatures due to greenhouse gases, changing rainfall patterns leading to severe water shortages, and deforestation have led to many areas facing drought conditions, [...] Read more.

Agricultural production is becoming increasingly difficult due to various environmental fluctuations brought on by climate change. Overall increase in atmospheric temperatures due to greenhouse gases, changing rainfall patterns leading to severe water shortages, and deforestation have led to many areas facing drought conditions, causing more stress for producing enough food crops to fulfil increasing global demand. This is also exacerbated by emerging phytopathogens causing severe disease outbreaks, making it difficult to control them without drastic measures. Excessive use of agrochemicals in these areas could lead to more ecological displacements and therefore, sustainable agricultural practices are required to avoid causing more harm. Microorganisms with climate-adaptive characteristics and qualities that would be helpful in acting as bioinoculants and biological control, could prove to be more successful in sustainably controlling emerging pathogens as well as improving the overall plant immunity and health in drought affected areas. We discuss how climate change driven changes in farming areas have made them vulnerable towards emerging pathogens, and highlight how biological control agents can be successfully utilized to possibly overcome this without causing more environmental damage. This review provides a background for future research by linking the climate adaptive characteristics of microorganisms with biocontrol and plant health improving capabilities and how they can effectively be used for eco-friendly agricultural practices in agroecosystems impacted by climate change. Full article

(This article belongs to the Special Issue Biocontrol Agents for Plant Pest Management)

23 pages, 9473 KB

Open AccessArticle

Digital Image Quantification of Rice Sheath Blight: Optimized Segmentation and Automatic Classification

by Da-Young Lee, Dong-Yeop Na, Yong Seok Heo and Guo-Liang Wang

Agriculture 2025, 15(23), 2478; https://doi.org/10.3390/agriculture15232478 (registering DOI) - 28 Nov 2025

Abstract

Rapid and accurate phenotypic screening of rice germplasms is crucial for identifying potential sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, diseased lesions of rice sheath blight (ShB)-infected plants are time-consuming, labor-intensive, and subject to human rater [...] Read more.

Rapid and accurate phenotypic screening of rice germplasms is crucial for identifying potential sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, diseased lesions of rice sheath blight (ShB)-infected plants are time-consuming, labor-intensive, and subject to human rater subjectivity. Here, we propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area. To be specific, we developed a Pixel Color- and Coordinate-based K-Means Clustering (PCC-KMC) algorithm utilizing the Mahalanobis distance metric, aimed at accurately segmenting symptomatic and non-symptomatic regions within rice stem images. The performance of PCC-KMC, combined with manual classification of the segmented regions, was evaluated using Lin’s concordance correlation coefficient (

ρ_{c}

) by comparing its results to visual measurements of ShB lesion height (cm) and to lesion/diseased area (cm₂) measured using ImageJ. Low bias (C_b) and high precision (r) were observed for absolute lesion height (C_b = 0.93, r = 0.94) and absolute symptomatic area (C_b = 0.98, r = 0.97) studies. Furthermore, to automatically classify the segmented regions produced by the PCC-KMC algorithm, we employed a convolutional neural network (CNN). Unlike conventional CNNs that require fixed-size image inputs, our CNN is designed to take the RGB histogram of each segmented region (a 1000 by 3 representation) as input and determine whether the region corresponds to ShB infection. This design effectively handles the arbitrary sizes and irregular shapes of segmentation regions generated by PCC-KMC. Our CNN was trained based on an 85%:15% composition for the training and testing dataset from a total of 168 ShB-infected stem sample images, recording 92% accuracy and 0.21 loss. PCC-KMC-CNN also showed high accuracy and precision for the absolute lesion height (C_b = 0.86, r = 0.90) and absolute diseased area (C_b = 0.99, r = 0.97) studies, indicating that PCC-KMC combined with automatic CNN-based classification performs very effectively. These results demonstrate that the potential of our methodology to serve as an alternative to the traditional visual-based ShB disease severity assessment and can be considered to be utilized for lab-scale, high-throughput phenotyping of rice ShB. Full article

(This article belongs to the Special Issue Exploring Sustainable Strategies That Control Fungal Plant Diseases)

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20 pages, 2973 KB

Open AccessArticle

A Virtual Water-Based Framework for Alleviating Regional Food Shortage in China: Modeling and Optimal Allocation

by Ziming Wang, Zhaoqiang Zhou, Tian Wang, Renjie Hou, Mo Li, Qinglin Li and Ping Xue

Agriculture 2025, 15(23), 2477; https://doi.org/10.3390/agriculture15232477 (registering DOI) - 28 Nov 2025

Abstract

The spatial mismatch between grain production and water resources in China poses significant challenges to food security. This study examines Heilongjiang Province, a major grain-producing region, to explore pathways for enhancing food security through virtual water redistribution. By calculating the virtual water content [...] Read more.

The spatial mismatch between grain production and water resources in China poses significant challenges to food security. This study examines Heilongjiang Province, a major grain-producing region, to explore pathways for enhancing food security through virtual water redistribution. By calculating the virtual water content of typical exported crops and integrating micro- and macroeconomic models, we coupled socio-economic benefits to develop a multi-objective allocation framework centered on “comprehensive benefits”. This framework forms the basis of a grain allocation model grounded in virtual water trade. Our study identifies eight typical grain-deficient regions, including Beijing and Shanghai, and demonstrates that Heilongjiang Province can meet their demands through virtual water transfers. The results reveal significant differences in allocation structures across crops and regions, reflecting heterogeneity in regional demands and resource endowments. This research provides theoretical insights and strategic directions for alleviating food security issues under imbalanced resource distribution, though practical application of the model requires further consideration of regional constraints. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

27 pages, 1082 KB

Open AccessArticle

Impact of Regional Agricultural Product Branding on Income Inequality: Evidence from Guangdong Province, China

by Jiyue Zhang, Hong Chen and Cheng Guo

Agriculture 2025, 15(23), 2476; https://doi.org/10.3390/agriculture15232476 (registering DOI) - 28 Nov 2025

Abstract

Agricultural product branding promotes regional economic development by enhancing brand value and market competitiveness, serving as a vital pathway for increasing farmers’ incomes and advancing the transformation of modern agriculture. This paper transcends one-dimensional analysis by examining the dual perspectives of urban-rural income [...] Read more.

Agricultural product branding promotes regional economic development by enhancing brand value and market competitiveness, serving as a vital pathway for increasing farmers’ incomes and advancing the transformation of modern agriculture. This paper transcends one-dimensional analysis by examining the dual perspectives of urban-rural income disparities and regional income gaps, thereby revealing the impact of regional agricultural product branding on income inequality. This study employs panel data from 82 counties in Guangdong Province spanning the years 2010 to 2023, comprising a total of 1148 observations, and treats the Ministry of Agriculture and Rural Affairs’ designation of “famous, special, excellent, and new” agricultural products as a policy hit. Employing a multi-period difference-in-differences model, it empirically examines the impact of regional agricultural product branding (RAPB) on income inequality. The study found the following: (1) RAPB narrowed the urban-rural income gap by 0.92% and Theil decreased significantly by about 15.3% on average. (2) Mechanism analysis indicates that RAPB mitigates income inequality through resource allocation effects, technological progress effects, and human capital accumulation effects. (3) Heterogeneity tests reveal that the inequality-alleviating effect of RAPB is most robust in regions focused on crop cultivation and areas with lower levels of agribusiness vitality, while its effect is weakened in dynamic entrepreneurial and high-yield regions. This study provides a new value metric for evaluating regional brand policies that balance efficiency and equity, revealing their core potential in promoting social fairness and coordinating urban-rural and regional development. Full article

(This article belongs to the Topic The Multidimensional Synergy Measures to Achieve Sustainable Regional Socio-Economic Development)

26 pages, 6535 KB

Open AccessArticle

Integrated Morphological, Physicochemical, Metabolomic, and Transcriptomic Analyses Elucidate the Mechanism Underlying Melon (Cucumis melo L.) Peel Cracking

by Yanping Hu, Yuxin Li, Tingting Zhang, Chongchong Wang, Baibi Zhu, Libo Tian, Min Wang and Yang Zhou

Agriculture 2025, 15(23), 2475; https://doi.org/10.3390/agriculture15232475 (registering DOI) - 28 Nov 2025

Abstract

Fruit peel cracking significantly reduces the commercial value of melons (Cucumis melo). To elucidate the underlying mechanisms of peel cracking, we conducted integrated investigations including morphological, physiological, metabolomic and transcriptomic analyses of cracked and non-cracked peels from the crack-resistant ‘Xizhoumi 17’ [...] Read more.

Fruit peel cracking significantly reduces the commercial value of melons (Cucumis melo). To elucidate the underlying mechanisms of peel cracking, we conducted integrated investigations including morphological, physiological, metabolomic and transcriptomic analyses of cracked and non-cracked peels from the crack-resistant ‘Xizhoumi 17’ and crack-susceptible ‘Xizhoumi 25’ cultivars. The parenchyma cells in ‘Xizhoumi 17’ exhibited a compact and well-organized arrangement, whereas those in ‘Xizhoumi 25’ displayed a loosely packed and disordered structure. Notably, cracked peels exhibited significantly higher levels of water-soluble pectin and lignin, along with increased cellulase, polygalacturonase, catalase, superoxide dismutase, and peroxidase activities. In contrast, protopectin, cellulose, and hemicellulose contents, as well as polyphenol oxidase activity, were markedly reduced compared to non-cracked peels. Metabolomic analysis revealed that the phenylpropanoid biosynthesis pathway is positively correlated with the progression of peel cracking. RNA-seq analysis revealed 119 and 82 differentially expressed genes associated with cell wall metabolism and lignin biosynthesis pathways, respectively. Collectively, these findings underscore the involvement of genes related to cell wall synthesis and degradation, as well as lignin synthesis, in modulating peel cracking through alterations in cell wall composition and structural stability, thereby offering practical implications for reducing melon peel cracking incidence via targeted molecular breeding of key genes regulating cell wall composition and the phenylpropanoid pathway. Full article

(This article belongs to the Section Crop Production)

26 pages, 2754 KB

Open AccessArticle

Chilo suppressalis Population Dynamics Forecasting by Exponential Smoothing Decomposition and Multi-Stream Network

by Chao He, Ziang Peng, Longhuang Peng, Yi Liu, Chengyuan Zhang, Lei Zhu, Siqiao Tan and Ling Zou

Agriculture 2025, 15(23), 2474; https://doi.org/10.3390/agriculture15232474 (registering DOI) - 28 Nov 2025

Abstract

Rice plays a pivotal role in global food security, particularly for Asian populations. However, its production is significantly threatened by insect pests, with Chilo suppressalis being a major pest in Asian rice-growing regions. Therefore, developing accurate predictive models for C. suppressalis outbreaks is [...] Read more.

Rice plays a pivotal role in global food security, particularly for Asian populations. However, its production is significantly threatened by insect pests, with Chilo suppressalis being a major pest in Asian rice-growing regions. Therefore, developing accurate predictive models for C. suppressalis outbreaks is essential. This study presents a novel time series forecasting model (named ESD-TripleStream) for C. suppressalis population dynamics based on a multi-stream structure, which addresses the limitations of existing approaches, which often omit the further decomposability of and the timestamp information in the time series. This model integrates Exponential Smoothing Decomposition (ESD) to separate the trend and seasonal components of time series data, along with a temporal feature stream to form a three-stream network to capture multi-scale periodic patterns and temporal dependencies. For our evaluation, we collected and constructed a novel dataset, referred to as HNRP-6R, which includes rice pest monitoring data from the past two decades (2000–2022) alongside 13 meteorological factors across six key rice producing regions in Hunan Province, southern China. ESD-TripleStream was evaluated across short-term and medium-term C. suppressalis population prediction scales using HNRP-6R, demonstrating state-of-the-art performance. Specifically, in short-term prediction, ESD-TripleStream achieved a 31.8% reduction in Mean Squared Error (MSE) and 26.55% reduction in Mean Absolute Error (MAE) compared to the PatchMLP model, while outperforming the transformer-based TimeXer by 14.43% in MSE and 9.8% in MAE. For medium-term prediction, ESD-TripleStream has both MSE and MAE significantly lower than those of baseline models such as P-sLSTM and xPatch. Furthermore, generalization tests on Nilaparvata lugens (N. lugens) population prediction demonstrated the model’s adaptability to diverse pest dynamics. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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13 pages, 3101 KB

Open AccessArticle

Pseudomonas syringae Population Recently Isolated from Winter Wheat in Serbia

by Renata Iličić, Marco Scortichini, Ferenc Bagi, Nemanja Pavković, Aleksandra Jelušić, Snežana Đorđević and Tatjana Popović Milovanović

Agriculture 2025, 15(23), 2473; https://doi.org/10.3390/agriculture15232473 (registering DOI) - 28 Nov 2025

Abstract

The aim of this study was to identify the causative agent of bacterial blight and basal glume rot of winter wheat that appeared in Serbia in 2023. To characterize the isolated bacteria (eight isolates in total), their cultural, biochemical, pathogenic, and genetic characteristics [...] Read more.

The aim of this study was to identify the causative agent of bacterial blight and basal glume rot of winter wheat that appeared in Serbia in 2023. To characterize the isolated bacteria (eight isolates in total), their cultural, biochemical, pathogenic, and genetic characteristics were examined. Based on the results of the LOPAT test, the isolates were classified into Pseudomonas Group Ia. The syrB and syrD genes were simultaneously detected in six wheat isolates—P0123, P0223, P0323, P0423, P0523, and P0823—while two isolates, P1123 and P1323, lacked both genes. Multilocus sequence typing (MLST) of the gapA, gltA, gyrB, and rpoD genes identified six isolates (P0123, P0223, P0323, P0423, P0523, and P0823) as Pseudomonas syringae pv. atrofaciens, whereas the remaining two isolates (P1123 and P1323) were most closely related to P. poae. Phylogenetic analysis revealed three genetically heterogeneous subgroups of P. syringae pv. atrofaciens among the wheat isolates from Serbia. Pathogenicity tests demonstrated that wheat isolates are able cause disease on wheat seedlings using three different inoculation methods: spraying the entire seedling, trimming the leaves before spraying, and wounding the leaves with multiple needles followed by spraying. Overall, isolates P0123 and P0423 were identified as the most virulent, inducing pronounced blight symptoms on wheat seedlings. In contrast, isolates P1123 and P1323 were weakly virulent and are therefore considered to be secondary or accompanying factors in plants already infected with more aggressive isolates, rather than primary pathogens responsible for disease development. This study contributes to a deeper understanding of the ecology, distribution, and pathogenic potential of bacterial communities associated with wheat blight disease in Serbia. Full article

(This article belongs to the Special Issue Endemic and Emerging Bacterial Diseases in Agricultural Crops)

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26 pages, 823 KB

Open AccessArticle

Economics of Conventional Dairy Manure Management in North Central Texas

by Edward Osei, Eunsung Kan, Syed H. H. Jafri, Ashley Lovell, Laura Henson, Kimberly Wellmann, James Muir, Jennifer Spencer and Zong Liu

Agriculture 2025, 15(23), 2472; https://doi.org/10.3390/agriculture15232472 (registering DOI) - 28 Nov 2025

Abstract

Manure management costs are a substantial component of overall costs on a modern dairy farm. Due to the slim margins of contemporary milk production operations, dairies are under constant pressure to increase milking herd sizes to take advantage of size economies that enable [...] Read more.

Manure management costs are a substantial component of overall costs on a modern dairy farm. Due to the slim margins of contemporary milk production operations, dairies are under constant pressure to increase milking herd sizes to take advantage of size economies that enable them to compete in the global marketplace. This study provides a current assessment of manure management and overall costs and returns on four standard sizes of dairies typical of the southern Great Plains, particularly north central Texas. The study is necessitated by the fact that the changing economic landscape has resulted in substantial changes in manure management practices. This study also forms the basis for additional analyses that will explore alternative value-added options for dairy manure management. We utilize the Farm-level Economic Model to holistically simulate the costs and returns of four representative dairy herd sizes—small (300 cows), medium (720 cows), large (1500 cows), and very large (5000 cows). Based on prevailing assumptions about land areas farmed and farm management practices, we find that dairy farms require between 0.18 and 0.4 ha/cow to manage manure based on crop nitrogen uptake rates, versus 0.67 to 0.95 ha/cow for crop phosphorus uptake rates. Manure application costs alone range from USD 55/cow (USD 225/ha) to USD 115/cow (USD 300/ha) depending on dairy size, but some of these costs are offset by fertilizer cost savings. Proportion of manure hauled offsite ranges from 9% to 67% for phosphorus-based applications, depending on herd size, and net incomes per cow are reduced by USD 60 to USD 100 (USD 4.33 to USD 8.27 per Mg of milk) if manure is applied based on phosphorus uptake rates as compared to nitrogen uptake rates of receiving crops. Generating a broad array of economically viable value-added product options from dairy manure would enable farmers to be more competitive in a market characterized by thin margins. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

15 pages, 3180 KB

Open AccessArticle

Comparative Transcriptomic and Metabolomic Profiling of Ovaries from Two Pig Breeds with Contrasting Reproductive Phenotype

by Sui Liufu, Jun Ouyang, Yi Jiang, Lanlin Xiao, Bohe Chen, Kaiming Wang, Wenwu Chen, Xin Xu, Caihong Liu and Haiming Ma

Agriculture 2025, 15(23), 2471; https://doi.org/10.3390/agriculture15232471 (registering DOI) - 28 Nov 2025

Abstract

Although numerous quantitative trait loci (QTLs) and candidate genes have been implicated in litter size in certain pig breeds, the genetic basis underlying the pronounced differences in reproductive capacity among breeds remains incompletely understood. To elucidate the underlying mechanisms responsible for the heterogeneity [...] Read more.

Although numerous quantitative trait loci (QTLs) and candidate genes have been implicated in litter size in certain pig breeds, the genetic basis underlying the pronounced differences in reproductive capacity among breeds remains incompletely understood. To elucidate the underlying mechanisms responsible for the heterogeneity in reproductive capacity, we performed integrated transcriptomic and metabolomic analyses on ovarian tissues from two pig breeds with contrasting litter sizes: Diannan Small-ear (DSE) pigs and Yorkshire (YK) pigs. YK pigs exhibited significantly higher total born piglets. Transcriptome analysis revealed that upregulated DEGs in YK ovaries were enriched in ovarian steroidogenesis, retinol metabolism, vitamin digestion/absorption, and folate biosynthesis. In contrast, DSE pigs showed enrichment in inflammatory and immune-related pathways. Furthermore, integrative transcriptomic and metabolomic analysis revealed that upregulated DEGs in YK pigs, such as STAR and COL3A1, and concurrently elevated metabolites (e.g., L-threonine, L-asparagine, L-proline, L-methionine, arachidonic acid, and progesterone) were jointly enriched in three key pathways: protein digestion and absorption, mineral absorption, and aldosterone synthesis and secretion. These genes and metabolites are implicated in granulosa cell and oocyte proliferation, maturation, and protection against oxidative damage. Our findings provide a theoretical foundation for future strategies aimed at improving reproductive performance through targeted modulation of key genes and metabolites. Full article

(This article belongs to the Special Issue Advancements in Reproductive Biotechnology and Nutritional Strategies in Livestock Production)

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34 pages, 7013 KB

Open AccessArticle

Construction and Application of Soil–Water Characteristic Curve Model Considering Water Mineralization Degree

by Xu Ding, Qian Xu, Feilong Jie, Mian Fan, Yanyan Ge and Sheng Li

Agriculture 2025, 15(23), 2470; https://doi.org/10.3390/agriculture15232470 (registering DOI) - 28 Nov 2025

Abstract

This study investigated the effects of irrigation water salinity on the soil–water characteristic curve (SWCC) using soil samples collected from a typical irrigated area in Yingjisha County, southern Xinjiang. The SWCC was determined experimentally via centrifugation. The correlation degree among influencing factors was [...] Read more.

This study investigated the effects of irrigation water salinity on the soil–water characteristic curve (SWCC) using soil samples collected from a typical irrigated area in Yingjisha County, southern Xinjiang. The SWCC was determined experimentally via centrifugation. The correlation degree among influencing factors was evaluated, and a goodness-of-fit assessment of mainstream traditional SWCC models was conducted using MATLAB 2021a. A modified Van Genuchten (VG) model incorporating the influence of irrigation water salinity was developed. The accuracy and reliability of the proposed model were validated through soil column infiltration experiments and numerical simulations. The results demonstrated that the original VG model provided the best fit for loam soils in southern Xinjiang, albeit with non-negligible deviations, indicating the need for further refinement. Significant correlations were identified between soil characteristic indices and model parameters, ranked in descending order of influence as follows: soil dry bulk density > clay content > inorganic salt content > silt content. Soils with higher clay and silt contents, along with greater bulk density, exhibited enhanced water retention capacity, resulting in a flatter SWCC. Although increased irrigation water salinity initially improved the soil’s water absorption capacity, the rate of enhancement gradually diminished with further increases in salinity, ultimately leading to a reduction in overall water retention performance. This study provides a theoretical foundation for the prevention and amelioration of saline soils and also supports the efficient utilization of water resources. Full article

(This article belongs to the Section Agricultural Soils)

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14 pages, 244 KB

Open AccessArticle

Change and Adaptation of Family Dairy Farming in the Context of Global Capitalism

by Jorge Alberto Cruz-Torres, Randy Alexis Jiménez-Jiménez, Valentín Efrén Espinosa-Ortíz, Marco Antonio Camacho-Escobar, Luis Manuel Chávez-Pérez and Mauricio Miguel-Estrada

Agriculture 2025, 15(23), 2469; https://doi.org/10.3390/agriculture15232469 - 28 Nov 2025

Abstract

Since the 1980s, Mexico has undergone profound economic and political transformations grounded in neoliberalism, reflected in the opening of the agri-food sector. As a result, imports of powdered milk increased, consolidating a corporate agri-food regime that has exerted structural pressure on small-scale dairy [...] Read more.

Since the 1980s, Mexico has undergone profound economic and political transformations grounded in neoliberalism, reflected in the opening of the agri-food sector. As a result, imports of powdered milk increased, consolidating a corporate agri-food regime that has exerted structural pressure on small-scale dairy producers, promoting processes of de-peasantization and proletarianization. This study analyzes the evolution of family dairy farming in Santa Elena, Michoacán, México, with the aim of identifying and analyzing the principal components of family structure, economic and productive rationality that have been maintained over time, and how they are modified to adapt the family dairy farming to the context of contemporary capitalism. It hypothesizes that changes in the main components of family structure, and productive and economic rationality of family dairy households are the result of strengthened peasant characteristics. Based on the analysis of census data of household production units (HPUs) in 2002 and 2018, a Principal Component Analysis (PCA) was conducted to characterize and identify changes in the productive and economic structure of these units. The component with variables linking family dairy farming to the market was the most significant and consistent over time. The remaining components varied. Feeding variables formed the second most important component in both studies, which changed its structure in 2018, focusing on minimizing operating costs and utilizing crop residues for feed. It is concluded that family dairy farming in Santa Elena, Michoacán, has adapted to the conditions of global agri-food capitalism without a clear transition toward productive intensification or specialization. Instead, family dairy farming has persisted through diversification strategies, self-management, and access to local markets, demonstrating the persistence of peasant economic rationality. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

23 pages, 2436 KB

Open AccessSystematic Review

Adoption and Perception of Precision Technologies in Agriculture: Systematic Review and Case Study in the PDO Wines of Granada, Southern Spain

by Jesús González-Vivar, Rita Sobczyk, Esteban Romero-Frías and Jesús Rodrigo-Comino

Agriculture 2025, 15(23), 2468; https://doi.org/10.3390/agriculture15232468 - 28 Nov 2025

Abstract

Precision technologies are increasingly relevant in contemporary agriculture, offering tools to enhance efficiency, sustainability, and decision-making. Their adoption is becoming particularly critical among vine-growers in the wine industry, a sector facing market pressures, climate change, and generational shifts. This study combines a systematic [...] Read more.

Precision technologies are increasingly relevant in contemporary agriculture, offering tools to enhance efficiency, sustainability, and decision-making. Their adoption is becoming particularly critical among vine-growers in the wine industry, a sector facing market pressures, climate change, and generational shifts. This study combines a systematic literature review with an empirical analysis of the PDO (Protected Designation of Origin) Wines of Granada (Southern Spain) to examine perceptions of precision agriculture technologies at both global and regional scales. The review included 607 articles published between 2015 and 2025 in English (indexed in ISI Web of Knowledge), identifying key factors influencing technology adoption. Using “perception” and “precision agriculture” as search terms, only 97 articles simultaneously addressed both concepts. At the regional level, a case study involving 22 wineries (with 37 stakeholders) in Granada province was conducted, focusing on socioeconomic barriers and environmental conditions such as altitude, climate, and soil type. Results revealed cross-scale consistencies regarding the importance of costs and perceived usefulness of new technologies (e.g., proximal sensors, satellite imagery), but divergences concerning the difficulties in accessing them and their cost. The findings highlight the need for supportive policies, targeted training, and practical demonstrations to facilitate adoption, thereby fostering innovation and sustainability, especially in the wine sector of the province of Granada. Integrating international and local evidence provides a framework for designing regional strategies tailored to promote precision technologies that improve efficiency, quality, and sustainability in wine production. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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27 pages, 4692 KB

Open AccessArticle

Spray Deposition, Drift and Equipment Contamination for Drone and Conventional Orchard Spraying Under European Conditions

by Artur Godyń, Waldemar Świechowski, Grzegorz Doruchowski, Ryszard Hołownicki, Andrzej Bartosik and Konrad Sas

Agriculture 2025, 15(23), 2467; https://doi.org/10.3390/agriculture15232467 - 28 Nov 2025

Abstract

In Europe, there is a growing interest in crop spraying using unmanned aerial vehicles (UAVs, drones), although current legislation imposes significant limitations on this technique. Spraying of orchard crops with drones remains particularly challenging due to the risks of spray drift and insufficient [...] Read more.

In Europe, there is a growing interest in crop spraying using unmanned aerial vehicles (UAVs, drones), although current legislation imposes significant limitations on this technique. Spraying of orchard crops with drones remains particularly challenging due to the risks of spray drift and insufficient deposition uniformity. This study evaluated spray deposition within tree canopies (in two application terms), airborne and sediment drift losses, and contamination of the spraying equipment. The performance of a medium-sized drone (ABZ Innovation L10, maximum take-off weight 29 kg) was compared at flight speeds of 1.8, 2.7, and 3.6 m·s⁻¹ with that of a conventional orchard sprayer (Munckhof axial sprayer with column attachment, operating at 1.7 m·s⁻¹). A fluorescent tracer (BF7G, 1200 g·ha⁻¹) was used in all trials, with spray volume rates of 27 or 40 L·ha⁻¹ for the drone and 400 L·ha⁻¹ for the sprayer. In most cases, deposition within the tree canopy was significantly lower for the drone. Poor uniformity of spray distribution was observed, especially between the upper and lower surfaces of collector plates with attached filter papers and between the top and bottom canopy zones. Airborne drift increased significantly with higher drone flight speeds, while sediment drift decreased. At 1.8 m·s⁻¹, both drift types were comparable to those from the conventional sprayer. Drone surface contamination was several times lower than that of the ground sprayer, even when accounting for differences in equipment surface area. Full article

(This article belongs to the Section Agricultural Technology)

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17 pages, 2962 KB

Open AccessArticle

Dynamics of Soil Bacterial Communities over Rice Growth Stages Under Different Fertilization Regimes in a Paddy Ecosystem

by Aiai Xu, Xiangzhou Zheng, Yushu Zhang, Qianqian Chen and Huangping Wang

Agriculture 2025, 15(23), 2466; https://doi.org/10.3390/agriculture15232466 - 28 Nov 2025

Abstract

The dynamic response of soil bacterial communities to fertilization throughout the entire crop growth cycle remains inadequately characterized. To address this, we conducted a long-term field experiment in Jiangle County, Fujian Province, China, and collected soil samples across four rice growth stages (tillering, [...] Read more.

The dynamic response of soil bacterial communities to fertilization throughout the entire crop growth cycle remains inadequately characterized. To address this, we conducted a long-term field experiment in Jiangle County, Fujian Province, China, and collected soil samples across four rice growth stages (tillering, elongation, filling and maturity) under five fertilization regimes: no fertilization (CK); chemical fertilizer (NPK); and NPK supplemented with extra nitrogen (NPKN), extra phosphorus (NPKP) and rice straw (NPKS). Bacterial communities were analyzed by high-throughput sequencing. Our results revealed that soil bacterial diversity decreased progressively throughout the growth stages, with fertilization exerting only a minor influence. Structural equation modeling (SEM) identified daily mean temperature (DMT) as the factor with the strongest direct and total effects on the diversity. In contrast, fertilization regimes were the primary determinant of the community structure. Mantel test and redundancy analysis (RDA) indicated that soil pH was the most important factor shaping the community structure. Soil bacterial network attributes also varied mainly with fertilization: fertilizer addition reduced the complexity but enhanced stability, with NPK and NPKS showing the greatest stability. Regarding rice yields, all fertilized treatments were comparable but considerably higher than CK. In conclusion, rice growth stages primarily influenced soil bacterial diversity, while fertilization regimes predominantly shaped the community structure and network attributes. Further, we recommend NPK and NPKS as optimal strategies for balancing crop production, agroecosystem sustainability and environmental health. Full article

(This article belongs to the Section Agricultural Soils)

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19 pages, 2104 KB

Open AccessArticle

Biological Control Properties of Two Strains of Priestia megaterium Isolated from Tar Spots in Maize Leaves

by Eric T. Johnson, Patrick F. Dowd and Jill K. Winkler-Moser

Agriculture 2025, 15(23), 2465; https://doi.org/10.3390/agriculture15232465 - 28 Nov 2025

Abstract

Priestia megaterium is a maize endophyte that may help the plant defend itself against bacterial and fungal pathogens. This study aimed to identify antimicrobials produced by two P. megaterium endophytes (FS10 and FS11) from maize and determine if seed coating with either strain [...] Read more.

Priestia megaterium is a maize endophyte that may help the plant defend itself against bacterial and fungal pathogens. This study aimed to identify antimicrobials produced by two P. megaterium endophytes (FS10 and FS11) from maize and determine if seed coating with either strain could increase resistance to pathogens. Volatiles emitted by both isolates reduced the hyphal growth of fungi by 17–76%. Gas chromatography analysis found that each strain emitted isovaleric acid (IVA) and 3-methyl-1-butanol (3MB). Volatiles produced by each isolate inhibited bacterial growth, especially Clavibacter michiganensis ssp. michiganensis (Cmm). IVA killed all Cmm cells at 208 µL L⁻¹, while 3MB inhibited Cmm growth by 51% at 208 µL L⁻¹. Diluted cell-free extracts from FS10 and FS11 cultures stopped growth of Cmm, Erwinia amylovora and Ustilago maydis but did not arrest growth of Fusarium verticillioides. The treatment of corn seeds with FS10 or FS11 reduced leaf damage by 38–84% in young plants caused by Bipolaris maydis, Colletotrichum graminicola (Ces.) G.W. Wilson 1914, Exserohilum turcicum and Pythium sylvaticum. FS10 and FS11 isolates exuded volatile and soluble compounds that were more effective in slowing growth of bacteria than fungi. It is likely that corn seed treatment with FS10 and FS11 triggers induced systemic resistance, which mitigates leaf damage caused by maize pathogens. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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21 pages, 6223 KB

Open AccessArticle

Research on Vegetation Dynamics and Driving Mechanisms in Karst Desertified Areas Integrating Remote Sensing and Multi-Source Data

by Jimin Tang, Yifei Liu, Yan Wang, Jiangxia Ye, Xiaojie Yin, Zhexiu Yu and Chao Zhang

Agriculture 2025, 15(23), 2464; https://doi.org/10.3390/agriculture15232464 - 27 Nov 2025

Abstract

Rocky desertification severely restricts socio-economic development in the karst regions. However, assessments linking karst rocky desertification and NPP changes over the long term and at high resolution are limited. This study aims to reveal the spatiotemporal patterns and driving mechanisms of NPP changes [...] Read more.

Rocky desertification severely restricts socio-economic development in the karst regions. However, assessments linking karst rocky desertification and NPP changes over the long term and at high resolution are limited. This study aims to reveal the spatiotemporal patterns and driving mechanisms of NPP changes in Wenshan Prefecture, addressing the scientific gap in quantitative process research and mechanism identification in karst desertification areas. We estimated vegetation NPP from 2000 to 2020 using remote sensing data and the CASA model. The Theil–Sen trend analysis and Mann–Kendall test were applied to assess temporal variation, while a Geographical Detector identified the dominant natural and human factors and their interactions shaping NPP spatial patterns. Our results showed that NPP increased overall by 4.07 gC m⁻² a⁻¹, alongside a general decline in rocky desertification. The most significant improvement occurred between 2010 and 2015, when rocky desertification shrank by 2224 km² and the dynamic rate reached 1.42%. Mean NPP reached 1057 gC m⁻² a⁻¹, with a “northwest high–southeast low” spatial pattern, and 77% of the region showed significant increases. Rocky desertification was most severe at elevations between 1000 and 2000 m. In the karst region, NPP is mainly controlled by natural factors, with soil depth and slope being the strongest influences. Human activity had the largest negative impact, and most factors interacted synergistically, where hydrothermal gradients and human disturbances more strongly suppressed NPP on steep, thin slopes than individually expected. These findings provide robust scientific evidence and practical decision-making support for ecological restoration, rocky desertification control and long-term sustainable development in Wenshan and other karst regions, highlighting the importance of continuous monitoring and adaptive management strategies to consolidate restoration achievements and guide future land-use planning and regional ecological policy. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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19 pages, 2525 KB

Open AccessArticle

The Effects of Sand-Fixing Agents and Trichoderma longibrachiatum on Soil Quality and Alfalfa Growth in Wind-Sand Soil

by Xiaolong Chen, Xu Li, Xiaofeng Shan, Zhi Dong and Chunchun An

Agriculture 2025, 15(23), 2463; https://doi.org/10.3390/agriculture15232463 - 27 Nov 2025

Abstract

The degradation of sandy land in Inner Mongolia presents a substantial threat to regional ecological security and the sustainable development of agriculture and animal husbandry. Planting alfalfa serves as a crucial recovery strategy; however, the inadequate capacity to retain water and nutrients impedes [...] Read more.

The degradation of sandy land in Inner Mongolia presents a substantial threat to regional ecological security and the sustainable development of agriculture and animal husbandry. Planting alfalfa serves as a crucial recovery strategy; however, the inadequate capacity to retain water and nutrients impedes this process. The current reliance on a singular microbial remediation method has demonstrated limited effectiveness in addressing the challenges posed by sandy soil. While traditional sand-fixing agents can improve soil nutrients, they lack biological activity. Furthermore, the synergistic mechanisms between these approaches and their ecological impacts within a single season remain poorly understood. This study involved a pot experiment utilizing wind-sand soil as the substrate to evaluate the soil physicochemical properties, enzyme activities, and microbial community structure associated with the stress resistance of alfalfa. The results indicated that the medium concentration of sand-fixing agent (1:75) exhibited optimal water retention performance, thereby creating a conducive growth microenvironment for Trichoderma longibrachiatum and mitigating fluctuations in surface temperature and humidity. The combined treatment significantly improved the alpha diversity of soil microorganisms, thereby improving the stability and stress resistance of the system. Through the synergistic approach of “sand fixation and water retention–nutrient activation–improved stress resistance”, the microenvironment of sandy land was effectively improved, promoting alfalfa growth. This method offers “environmentally friendly and synergistic” technical support for the efficient cultivation and ecological restoration of alfalfa in sandy regions, while also contributing to the high-quality development of grassland animal husbandry. Full article

(This article belongs to the Section Agricultural Soils)

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24 pages, 19115 KB

Open AccessArticle

Estimation of Carbon Sequestration Capacity of Cultivated Land Based on Improved CASA-CGC Model—A Case Study of Anhui Province

by Lina Zhang, Chun Dong, Rui Zhang, Kaifang Shi, Yingchun Wang and Bao Li

Agriculture 2025, 15(23), 2462; https://doi.org/10.3390/agriculture15232462 - 27 Nov 2025

Abstract

Quantifying carbon sequestration in cultivated land ecosystems is essential for achieving carbon neutrality and ensuring food security, yet current models often fail to capture the complex interactions between crop phenology and environmental factors at regional scales. This paper proposed an improved CASA-CGC model [...] Read more.

Quantifying carbon sequestration in cultivated land ecosystems is essential for achieving carbon neutrality and ensuring food security, yet current models often fail to capture the complex interactions between crop phenology and environmental factors at regional scales. This paper proposed an improved CASA-CGC model that couples crop phenological parameters with photosynthetic physiological processes, enabling precise carbon sink accounting at the growth cycle scale of cultivated land ecosystems. Results indicate that the carbon sequestration capacity of cultivated land in the province significantly increased from 2010 to 2022, with an average increase of 163.04 g C m⁻², and the spatial pattern showed a centralized evolution characteristic. Model validation showed that the accuracy of the CASA-CGC model is significantly better than traditional methods. Compared with remote sensing inversion products and 93 ground measurement point data, the improved CASA-CGC model increased the R² by 0.155 and reduced the RMSE by 4.19 compared with the tr-CASA model. The innovative introduction of the GeoDetector model reveals that the nonlinear interaction between natural and human factors dominates the carbon sequestration process (accounting for 60%), with the interaction effect between altitude and cropping system configuration being the strongest (q = 0.312), confirming that humans can significantly amplify the potential of natural carbon sinks by optimizing cropping systems. Full article

(This article belongs to the Topic Carbon and Nitrogen Cycling in Agro-Ecosystems and Other Anthropogenically Maintained Ecosystems—2nd Edition)

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15 pages, 1820 KB

Open AccessArticle

Probabilistic Deep Learning Framework for Greenhouse Microclimate Prediction with Time-Varying Uncertainty and Covariance Analysis

by Woo-Joo Choi and Myongkyoon Yang

Agriculture 2025, 15(23), 2461; https://doi.org/10.3390/agriculture15232461 - 27 Nov 2025

Abstract

Although greenhouse microclimates typically exhibit gradual and near-linear transitions, abrupt fluctuations in external weather conditions and actuator operations introduce nonlinear dynamics that complicate accurate interpretation and prediction. Predicting greenhouse microclimate is a key element for achieving stable and energy efficient crop production, particularly [...] Read more.

Although greenhouse microclimates typically exhibit gradual and near-linear transitions, abrupt fluctuations in external weather conditions and actuator operations introduce nonlinear dynamics that complicate accurate interpretation and prediction. Predicting greenhouse microclimate is a key element for achieving stable and energy efficient crop production, particularly in strawberry greenhouse. However, existing greenhouse microclimate deterministic prediction models do not adequately reflect the nonlinear, time-varying characteristics of greenhouses and the inherent uncertainty in data, limiting probabilistic decision-making. In this study, we developed a probabilistic deep learning framework to estimate and interpret uncertainty while simultaneously predicting greenhouse microclimate quantitatively. The proposed one-dimensional convolutional neural network model learned the time-series characteristics of greenhouse internal and external environmental information and control data, predicting a total of nine parameters, including three-dimensional predicted values 3 h later and six-dimensional covariance elements. The model demonstrated high sharpness and calibration performance, with an average R² of 0.93, a negative log likelihood of 2.08, and a Coverage 90% of 0.901 for three microclimates. In addition, the estimated covariance matrix was used to interpret the time-varying correlations between microclimate variables, confirming local simultaneous variability not captured by global correlation analysis. These results suggest that the model in this study can provide greenhouse operators with explainable uncertainty interpretation and robust control decision support information. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

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