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Volume 16
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Agriculture, Volume 16, Issue 8 (April-2 2026) – 94 articles

Cover Story (view full-size image): This cover illustrates a decision-oriented framework for selecting suitable methods to genotype the bovine CSN2 A1/A2 polymorphism. The A1 and A2 β-casein variants influence breeding strategies and A2-labelled dairy production, yet the most appropriate analytical method depends on sample type, decision risk, and required sensitivity. Screening methods such as AS-PCR and HRM are suitable for routine herd testing, whereas sequencing and digital PCR offer higher confidence for irreversible breeding decisions and mixed-sample authentication. By integrating analytical performance with operational constraints and error consequences, the proposed framework supports transparent, fit-for-purpose genotyping across breeding, diagnostics, and dairy supply chains. View this paper
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27 pages, 13004 KB  
Article
Classification of Wheat Varieties Using Fourier-Transform Infrared Spectroscopy and Machine-Learning Techniques
by Mahtem Teweldemedhin Mengstu, Alper Taner and Neluș-Evelin Gheorghiță
Agriculture 2026, 16(8), 914; https://doi.org/10.3390/agriculture16080914 - 21 Apr 2026
Viewed by 777
Abstract
The combination of Fourier-transform infrared (FTIR) spectroscopy and machine learning gives a promising result in wheat variety classification. This study aimed to evaluate the contributions of distinct spectral regions and their combinations to classification performance. Out of the full raw spectra of four [...] Read more.
The combination of Fourier-transform infrared (FTIR) spectroscopy and machine learning gives a promising result in wheat variety classification. This study aimed to evaluate the contributions of distinct spectral regions and their combinations to classification performance. Out of the full raw spectra of four bread wheat varieties, namely Altindane, Cavus, Flamura-85, and Nevzatbey, 15 spectral datasets were prepared. Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) models were trained and analyzed. The highest classification performance was obtained using spectral regions associated with protein and lipid bands. The highest average accuracy of 0.9895 was shown by the SVM model, while the ANN produced comparable results with lower variability. Additionally, Variable Importance in Projection (VIP) analysis identified the most influential spectral bands in the protein (Amide II, ~1542 cm−1) and carbonyl (1744–1715 cm−1) regions. These findings indicate that classification is driven by chemically meaningful features rather than purely statistical patterns. The approach followed in this study provides an insight that, in FTIR-based classification, when rigorously evaluated using nested cross-validation, spectral region selection can outweigh model complexity. This approach demonstrates strong potential for rapid and non-destructive assessment, especially for real-time applications in grain processing and automated sorting systems. Full article
(This article belongs to the Special Issue Integrating Spectroscopy and Machine Learning for Crop Phenotyping)
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23 pages, 19480 KB  
Article
A Multi-Spatial Scale Integration Framework of UAV Image Features and Machine Learning for Predicting Root-Zone Soil Electrical Conductivity in the Arid Oasis Cotton Fields of Xinjiang
by Chenyu Li, Xinjun Wang, Qingfu Liang, Wenli Dong, Wanzhi Zhou, Yu Huang, Rui Qi, Shenao Wang and Jiandong Sheng
Agriculture 2026, 16(8), 913; https://doi.org/10.3390/agriculture16080913 - 21 Apr 2026
Cited by 1 | Viewed by 865
Abstract
Soil salinization is one of the primary forms of land degradation in arid and semi-arid regions, severely constraining agricultural production in Xinjiang’s oases. Unmanned aerial vehicle (UAV) imagery provides an effective means for precise monitoring of soil salinization, with image spatial resolution being [...] Read more.
Soil salinization is one of the primary forms of land degradation in arid and semi-arid regions, severely constraining agricultural production in Xinjiang’s oases. Unmanned aerial vehicle (UAV) imagery provides an effective means for precise monitoring of soil salinization, with image spatial resolution being a key factor affecting assessment accuracy. However, traditional single-scale remote sensing monitoring methods rely solely on spectral and textural features at the leaf scale (0.1 m resolution captures leaf-scale characteristics), neglecting the contribution of multi-scale features (single-row canopy scale and single-membrane-covered area scale (6-row crop canopy)) to soil salinity. For instance, 0.5–1 m reflects single-row canopy scale, while 2 m reflects single-membrane-covered area scale. Therefore, this study developed a multi-scale UAV imagery and machine learning framework to enhance soil electrical conductivity prediction accuracy. This study focuses on oasis cotton fields in Shaya County, Xinjiang. Based on UAV multispectral imagery, we resampled data to generate eight datasets at different spatial resolutions: 0.1, 0.5, 1, 1.5, 2, 2.5, 5, and 10 m. For each resolution, we calculated 21 spectral indices and 48 texture features to construct a feature set. At both single and multispatial scales, spectral indices, texture features, and their spectral-texture fusion features were constructed. Combining these with Backpropagation Neural Network (BPNN), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGBoost) models, a soil EC estimation framework was developed. The impact of three feature combination schemes on cotton field soil conductivity estimation using single-scale UAV imagery was compared. The accuracy of soil EC estimation for cotton fields was compared between multi-spatial scale and single-scale UAV image features. The optimal combination strategy for a multi-spatial scale and multiple features was determined. Results indicate that combining spectral and texture features yields the highest estimation accuracy for cotton field soil electrical conductivity in single-scale analysis. Multi-spatial scale image features outperform single-scale image features in estimating cotton field soil electrical conductivity accuracy. By comparing different feature combinations, when integrating 0.5 m spatial-scale spectra (S1, EVI, DVI, NDVI, Int1, SI) with 0.1 m texture features (RE1_ent, R_cor, RE1_cor, G_hom, B_mea, R_con, NIR_con), the XGBoost model achieved the optimal prediction accuracy (R2 = 0.693, RMSE = 0.515 dS/m), outperforming the methods using multiple features at a single scale. This study developed a novel multi-scale image feature fusion technique to construct a machine learning model. This method describes the image characteristics of soil electrical conductivity at different geographical scales, providing a reference approach for the rapid and accurate prediction of soil electrical conductivity in arid regions. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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14 pages, 1538 KB  
Article
Blanching Effects of Undaria pinnatifida on Chemical Compositions, Rumen Fermentation Characteristics, Greenhouse Gas Emissions, and Rumen Microbes In Vitro
by Chang-Hyun Baeg, Seung-Min Jeong, Arrynda Rachma Dyasti Wardani, Ji-Yoon Kim, Bu-Gil Choi, Young-Ho Joo, Hye-Seong Kim, Yong-Hyun Do, Jung-Jun Park and Sam-Churl Kim
Agriculture 2026, 16(8), 912; https://doi.org/10.3390/agriculture16080912 - 21 Apr 2026
Cited by 1 | Viewed by 786
Abstract
This study aims to evaluate the blanching process of wasted Undaria pinnatifida as a ruminant feed source by assessing chemical compositions, in vitro nutrient digestibility, rumen fermentation characteristics, greenhouse gas emissions, and rumen microbes. The blanching process was conducted at different temperatures (15 [...] Read more.
This study aims to evaluate the blanching process of wasted Undaria pinnatifida as a ruminant feed source by assessing chemical compositions, in vitro nutrient digestibility, rumen fermentation characteristics, greenhouse gas emissions, and rumen microbes. The blanching process was conducted at different temperatures (15 vs. 80 vs. 90 °C) and times (2 vs. 4 min) to assess the chemical and mineral contents. Supplementation levels of U. pinnatifida (0 vs. 0.5 vs. 1 vs. 2%) were observed with the blanching process (non-blanching (NBL) vs. blanching (LOS)). Increasing blanching temperature and time decreased (p < 0.05) dry matter, crude ash, and the mineral contents, including sodium, phosphorus, and arsenic. Moreover, LOS treatment increased (p < 0.01) in vitro dry matter and neutral detergent fiber digestibility, ruminal pH, and the acetate-to-propionate ratio, but reduced (p < 0.01) CH4 (mL/g NDFD). Additionally, 2% of LOS treatment reduced the abundance of protozoa, fungi, fibrolytic microbes, methanogenic archaea, Methanobrevibacter ruminantium, Methanosarcina barkeri, and Methanosphaera stadtmanae (p < 0.01). Therefore, blanching at 80 °C for 2 min improved the nutritional profile by reducing antinutritional minerals. Subsequent in vitro fermentation suggested that supplementing the diet with 0.5–1% of LOS improved digestibility and altered fermentation, potentially reducing methane yield (per NDFD). Full article
(This article belongs to the Special Issue Valorization of Agri-Food Waste Bioresources for Sustainable Livestock Feeding—2nd Edition)
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15 pages, 2723 KB  
Article
Overcoming the Salinity Bottleneck: Biochar-Induced Soil Organic Carbon Modulates Wheat Yield via Contrasting Pathways in a Coastal Saline Soil
by Tong Liu, Shengchao Hu, Xinliang Dong, Boyuan Lou, Wenxin Bian, Hongyong Sun, Jintao Wang, Xiaojing Liu, Chengrong Chen and Yunying Fang
Agriculture 2026, 16(8), 911; https://doi.org/10.3390/agriculture16080911 - 21 Apr 2026
Viewed by 793
Abstract
Biochar amendment holds promise for improving saline soils, yet its efficacy is often constrained by the uncertainty of application rates. In this study, a large field trial and associated statistical modeling were conducted to explore the mechanisms by which biochar affects wheat yield [...] Read more.
Biochar amendment holds promise for improving saline soils, yet its efficacy is often constrained by the uncertainty of application rates. In this study, a large field trial and associated statistical modeling were conducted to explore the mechanisms by which biochar affects wheat yield in coastal saline soils of northern China. Results showed that biochar application significantly increased soil organic carbon (SOC) content (R2 = 0.615, p < 0.001) but induced marked spatial heterogeneity across the field, with the coefficient of variation (CV) reaching 30.2%. Given the difficulty of uniformly applying biochar in the field, subplot-level SOC was used as a proxy for effective biochar distribution. Stepwise regression identified soil electrical conductivity (EC) as the dominant yield constraint (standardized coefficient = −0.69), rather than water and nutrients, and a quadratic relationship was observed between SOC and EC. Structural equation modeling (SEM) further suggested a trade-off: SOC was associated with higher yield through reduced bulk density (BD) (path coefficient = −0.603), whereas high SOC levels were also associated with increased EC under this coastal saline field setting (path coefficient = 0.243), thereby indirectly constraining growth. Consequently, the agronomic response showed a threshold-like transition: the peak wheat yield occurred at an SOC threshold of 13.87 g kg−1 (equivalent to 44.41 t ha−1), which exceeded the point of minimum salinity (11.71 g kg−1, equivalent to ~29.90 t ha−1 biochar). These results suggest that the agronomic benefit of biochar in saline soils depends on maintaining application within an estimated beneficial buffering zone. Full article
(This article belongs to the Special Issue Effects of Biochar on Soil Improvement and Crop Production)
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20 pages, 2817 KB  
Article
Enhancing the Sustainability of Apple Farming Utilizing Climate-Smart Agricultural (CSA) Practices
by Tryfon Kekes, Fotini Drosou, Georgia Frakolaki, Christos Boukouvalas, Nickolaos M. Panagiotou, Jon Bienzobas, Alexia Zabalza and Magdalini Krokida
Agriculture 2026, 16(8), 910; https://doi.org/10.3390/agriculture16080910 - 21 Apr 2026
Cited by 1 | Viewed by 838
Abstract
The main scope of the present study is to assess the environmental and economic outcomes of applying distinct Climate-Smart Agricultural (CSA) practices in apple cultivation. Thus, four different CSA practices, including organic farming, cover crops, floral bands, and grazing, were selected, and their [...] Read more.
The main scope of the present study is to assess the environmental and economic outcomes of applying distinct Climate-Smart Agricultural (CSA) practices in apple cultivation. Thus, four different CSA practices, including organic farming, cover crops, floral bands, and grazing, were selected, and their environmental and economic performance was evaluated and compared to that of a conventional apple orchard system (baseline). Specifically, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methodologies were applied to assess the environmental and economic sustainability of the studied systems, respectively. Among the studied practices, grazing exhibited the best environmental performance among the modeled scenarios (approximately 25% decrease in greenhouse gas emissions compared to the baseline under the assumed conditions), followed by organic farming that significantly decreased eutrophication- and ecotoxicity-related impacts. Similarly, organic farming and grazing exhibited the best economic performance in the concept of the present study, with the total profit per hectare rising to approximately 5300 € and 4300 €, respectively, compared to the value of 3700 € of the conventional apple orchard. The results suggest that the implementation of CSA practices has the potential to improve the environmental and economic performance of apple orchards under the modeled conditions. Full article
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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24 pages, 6382 KB  
Article
Simulation Analysis and Test of Tracked Chassis of Silage Harvester in Hilly and Mountainous Areas
by Pengfei Li, Keping Zhang, Jiuxin Wang, Junqian Yang and Xiaokang Li
Agriculture 2026, 16(8), 909; https://doi.org/10.3390/agriculture16080909 - 21 Apr 2026
Cited by 1 | Viewed by 838
Abstract
Aiming at the problem of the insufficient passability and stability of the tracked chassis of silage harvesters caused by complex hilly and mountainous areas and a severe working environment, the crawler chassis of self-propelled silage harvesters was taken as the research object, the [...] Read more.
Aiming at the problem of the insufficient passability and stability of the tracked chassis of silage harvesters caused by complex hilly and mountainous areas and a severe working environment, the crawler chassis of self-propelled silage harvesters was taken as the research object, the straight-line driving, longitudinal climbing, and lateral climbing processes of the chassis were theoretically analyzed, and the critical parameters that affect the normal climbing of the chassis were calculated. Meanwhile, the multi-body dynamics model of the tracked chassis was established by using the software SolidWorks 2020 and RecurDyn 2023, and its climbing and obstacle crossing performance were analyzed. The relevant motion parameters of the tracked chassis suitable for longitudinal and transverse slopes in hilly and mountainous areas were obtained, and field tests were conducted on the tracked chassis to verify the reliability of the simulation model. According to the simulation results, the tracked chassis achieves ultimate slope angles of 28° longitudinally and 23° laterally. It demonstrates the capability to navigate 140 mm high ridges and 250 mm wide trenches smoothly, while its straight-line driving offset rate conforms to prevailing agricultural machinery industry standards. Field test results indicated that the tracked chassis achieved a maximum longitudinal climbing angle of 26°. The relative error of less than 8% between the experimental and simulated data confirms a strong correlation. The maximum offset rate for straight-line travel is 1.95%, meeting the requirements of the agricultural machinery industry standards. The test verified the feasibility of the dynamic model of the crawler chassis of the silage harvester, providing a theoretical basis and technical support for the optimal design of the crawler chassis of the self-propelled silage harvester in hilly and mountainous areas. Full article
(This article belongs to the Section Agricultural Technology)
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23 pages, 906 KB  
Article
Building Climate-Resilient Farming Systems Through Agroecological Practices: Evidence from Mango Production in Southern Ethiopia
by Fasikaw Belay Mihretu, Melkamu Alemayehu, Mengistie Mossie, Yayeh Bitew, Bayu Enchalew and Tadele Tefera
Agriculture 2026, 16(8), 908; https://doi.org/10.3390/agriculture16080908 - 20 Apr 2026
Cited by 1 | Viewed by 1346
Abstract
To combat climate change, farmers want to develop sustainable agriculture that enhances food production while strengthening their capacity to cope with extreme weather events and pest and disease pressures. Promoting agroecological farming practices is a promising approach in enhancing sustainability and strengthening the [...] Read more.
To combat climate change, farmers want to develop sustainable agriculture that enhances food production while strengthening their capacity to cope with extreme weather events and pest and disease pressures. Promoting agroecological farming practices is a promising approach in enhancing sustainability and strengthening the climate-resilient farming systems. Recent research often overlooks to what extent the agroecological farming practices (AFP) provide a measurable advantage over non-AFP methods under increasing environmental challenges. In this regard, this study compares the extent of climate resilience between AFP mango-based farming systems and non-AFP mango-based farming systems in southern Ethiopia. AFP adopters applied ecological principles like intercropping, integrated pest management, agroforestry, canopy management, varietal diversity, and water and soil preservation to enhance biodiversity and soil health, and boost productivity and ecosystem services. The study employed a mixed-method design, drawing on the data from 395 selected households. The resilience of AFP and non-AFP farming systems was assessed by computing the 13 agroecosystem indicators of climate resilience using the Self-evaluation and Holistic Assessment of Climate Resilience of Farmers and Pastoralists (SHARP+) tool. Households in AFP mango-based farming system demonstrated greater diversification in agricultural production system compared to those in non-AFP mango farming system. The analysis of climate resilience indicators showed that the mango production systems under the AFP were more climate-robust than their conventional systems. Both the compound resilience score and the household resilience index showed that the mango farming systems under AFP substantially enhanced climate resilience. Hence, coordinated supports from the extension services, NGOs, and researchers are needed to scale up these benefits of AFP. Strengthening the AFP mango farming requires addressing the key barriers such as market access, input availability, and crop diversification strategies. This paper identifies important avenues for further AFP research in Sub-Saharan African countries. Full article
(This article belongs to the Section Agricultural Systems and Management)
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17 pages, 2621 KB  
Article
Pot Experiments Overestimate Mercury Accumulation in Rice: Evidence from Multi-Year Field Validation
by Lingxiao Zhang, Jinlong Dong, Xiao Ma, Xiaoquan An, Feiyu Luo, Yue Gao, Ziliang Zhang, Xun Li, Zhirou Shu and Zengqiang Duan
Agriculture 2026, 16(8), 907; https://doi.org/10.3390/agriculture16080907 - 20 Apr 2026
Viewed by 1047
Abstract
The uptake and accumulation of mercury (Hg) in rice poses a serious threat to food safety. Pot experiments are widely used to screen for low-Hg-accumulating cultivars, yet their reliability in predicting field performance remains uncertain. This study evaluated pot-based screening by (1) comparing [...] Read more.
The uptake and accumulation of mercury (Hg) in rice poses a serious threat to food safety. Pot experiments are widely used to screen for low-Hg-accumulating cultivars, yet their reliability in predicting field performance remains uncertain. This study evaluated pot-based screening by (1) comparing Hg uptake in rice grown in freshly processed versus aged soil; (2) contrasting Hg accumulation in the same cultivars grown in pots versus at two field sites; and (3) isolating micro-environmental effects by burying pots in situ. A total of 22 rice cultivars were used during 2021–2023 in this study. Pot systems, regardless of soil treatment, failed to replicate field accumulation patterns, yielding significantly greater Hg concentrations in brown rice (up to 59.24 ng g−1) than field conditions (maximum 32.33 ng g−1). Cultivar rankings derived from pot experiments showed little or no correlation with field rankings, indicating that performance is not transferable across environments. Random forest analysis identified elevated soil temperature and reduced light intensity as key artificial factors driving overestimation in pots, explaining 15.68% (total Hg) and 21.65% (methylmercury) of the variation. We conclude that pot experiments—due to soil disturbance and altered microclimates—overestimate Hg accumulation potential and show limited predictive capacity under the tested conditions. Therefore, field validation across multiple sites and seasons is essential for accurate mercury risk assessment and region-specific cultivar recommendation. Full article
(This article belongs to the Topic Environmental Pollution in Modern Agriculture: Causes, Effect, and Control)
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13 pages, 1208 KB  
Article
Natural Factors Driving Yield Variability of Camelina sativa L. Crantz and Brassica carinata L. Brown Yield on Sandy-Textured Soils—Case Study from Poland
by Bartłomiej Glina, Danuta Kurasiak-Popowska, Tomasz Piechota, Monika Grzanka, Sylwia Mikołajczyk, Agnieszka Tomkowiak, Kinga Stuper-Szablewska and Katarzyna Rzyska-Szczupak
Agriculture 2026, 16(8), 906; https://doi.org/10.3390/agriculture16080906 - 20 Apr 2026
Viewed by 694
Abstract
Climate change-induced variability in temperature and precipitation increasingly constrains crop production on sandy-textured soils with low water-holding capacity and limited nutrient retention. Such soils, classified as Brunic Arenosols, are widespread across the temperate climate zone of Central Europe, particularly in post-glacial landscapes, where [...] Read more.
Climate change-induced variability in temperature and precipitation increasingly constrains crop production on sandy-textured soils with low water-holding capacity and limited nutrient retention. Such soils, classified as Brunic Arenosols, are widespread across the temperate climate zone of Central Europe, particularly in post-glacial landscapes, where they constitute a significant proportion of marginal agricultural lands. This study evaluated the relative influence of growing-season weather conditions and selected soil physicochemical properties on the yield of Camelina sativa and Brassica carinata cultivated under low-input management on Brunic Arenosols in northwestern Poland during the 2023 season. Yields varied markedly among sites. Camelina sativa produced yields from 300 to 930 kg ha−1, with the highest yield recorded at the site characterized by higher BS and phosphorus availability. Brassica carinata produced yields from 0 to 370 kg ha−1, including complete yield loss at one location due to severe pathogen infestation. Spearman’s correlation analysis revealed that temperature was a key determinant for both crops (r = 0.77 for C. sativa; r = 0.82 for B. carinata). For Camelina sativa, yield was strongly associated with BS (r = 0.80) and available P (r = 0.69), whereas Brassica carinata was more sensitive to climatic variability, showing a negative relationship with precipitation (r = −0.63). The results indicate species-specific responses to soil fertility and weather conditions under water- and nutrient-limited conditions typical of Central European sandy soils. While Camelina sativa performance was more closely linked to soil chemical status, Brassica carinata appeared predominantly climate-driven. These findings highlight the broader relevance of the study for temperate regions of Central Europe and support the integration of soil fertility management with climate-adaptive strategies when introducing alternative oilseed crops to marginal lands. Full article
(This article belongs to the Section Agricultural Soils)
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25 pages, 6626 KB  
Article
Foliar Magnesium Supplementation as a Strategy to Mitigate Salt Stress in Guava (Psidium guajava L.) Cultivars: Physiological and Growth Responses
by Luan Cordeiro de Souza Barbosa, Paulo Cássio Alves Linhares, Maria Mayanna Xavier Cavalcante, Daniella Xavier Maia, Gabriel Sidharta dos Santos Rego, Rita de Cássia do Nascimento Medeiros-Sá, Alexandre Xavier de Oliveira, Diogo Santos Cavalcante, Alex Alvares da Silva, Kleane Targino de Oliveira Pereira, Salvador Barros Torres, Miguel Ferreira Neto, Agda Malany Forte de Oliveira, Alberto Soares de Melo and Francisco Vanies da Silva Sá
Agriculture 2026, 16(8), 905; https://doi.org/10.3390/agriculture16080905 - 20 Apr 2026
Cited by 1 | Viewed by 772
Abstract
The guava tree (Psidium guajava L.) is a tropical fruit tree of worldwide importance; however, the salinity of irrigation water severely limits its development in semi-arid regions. However, magnesium (Mg) can mitigate this stress by promoting plant photosynthetic activity. The objective was [...] Read more.
The guava tree (Psidium guajava L.) is a tropical fruit tree of worldwide importance; however, the salinity of irrigation water severely limits its development in semi-arid regions. However, magnesium (Mg) can mitigate this stress by promoting plant photosynthetic activity. The objective was to evaluate the effect of foliar Mg in mitigating saline stress on photosynthesis and the growth of guava cultivar seedlings. The experiment was conducted in a randomized complete block design, in a 2 × 2 × 3 factorial scheme, with two guava cultivars (Kumagai and Paluma), two irrigation water salinity levels (a low-salinity control—0.5 dS m−1, and salt stress—2.5 dS m−1), and three doses of foliar Mg (0, 1, and 2 mL L−1), and six replications. A salinity of 2.5 dS m−1 reduced growth and gas exchange in both cultivars, with a reduction of approximately 30% in total dry mass, and 16% in CO2 assimilation rate. Supplementation with 1 mL L−1 of Mg attenuated the effects of stress, stimulating chlorophyll synthesis and gas exchange, reducing approximately leaf temperature in 3.5%, and vapor pressure deficit (VPD) in 12%. The Paluma cultivar was more responsive to Mg under salinity, with improved CO2 assimilation rate, stomatal control, and water use efficiency. Kumagai showed greater growth in height and diameter with 1 mL L−1 under stress. Foliar application of magnesium (1 mL L−1) is a promising strategy to produce guava seedlings under saline stress. Full article
(This article belongs to the Special Issue Agricultural Crops Subjected to Drought and Salinity Stress—2nd Edition)
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26 pages, 7056 KB  
Article
Structural Constraints and Institutional Support in Agricultural Production Systems: Evidence from Farmers in Northern Mexico
by Omar Alfonso Rivera-Hernández, Emily García-Montiel, Henry German Chico-León, Pablito Marcelo López-Serrano, Hugo Ramírez-Aldaba, César Guillermo García-González and Griselda Vázquez-Quintero
Agriculture 2026, 16(8), 904; https://doi.org/10.3390/agriculture16080904 - 20 Apr 2026
Viewed by 907
Abstract
This study examines how structural constraints and institutional support are associated with the configuration of agricultural production systems in Durango, Mexico. Using survey data from 362 farmers, multidimensional indices capturing productive restrictions, export barriers, technification gaps, and support needs were constructed using binary [...] Read more.
This study examines how structural constraints and institutional support are associated with the configuration of agricultural production systems in Durango, Mexico. Using survey data from 362 farmers, multidimensional indices capturing productive restrictions, export barriers, technification gaps, and support needs were constructed using binary indicators and normalized to a 0–1 scale, with internal consistency assessed through the Kuder–Richardson coefficient. To analyze structural dynamics, Ordinary Least Squares (OLS) and fractional logit models were used to estimate determinants of structural pressure, while a logistic regression model was applied to examine differences in structural positioning across production regimes identified through cluster analysis. The results show that larger productive scale is associated with higher exposure to regulatory and commercialization pressures, indicating that farm expansion tends to coincide with increased structural complexity rather than automatic improvements in structural conditions. More importantly, institutional linkage remains positively and statistically significant in the logistic model (OR = 9.085; p = 0.015), even after controlling for technification, structural constraints, export barriers, labor intensity, and farming experience. These findings indicate that institutional connectivity is positively associated with differentiated structural configurations among production units and are consistent with the interpretation that institutional support is associated with variation in structural conditions in semi-arid agricultural systems, beyond the effects of technological endowment or scale alone. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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20 pages, 4476 KB  
Article
Soil-Applied Selenium Reduces Cadmium Accumulation and Alters Selenium Enrichment in Artemisia selengensis in Cd-Contaminated Soil
by Ziliang Zhang, Han Tang, Yu Zhang, Jian Lin, Renshan Pan, Lingxiao Zhang, Xiao Ma, Jinlong Dong, Xun Li and Zengqiang Duan
Agriculture 2026, 16(8), 903; https://doi.org/10.3390/agriculture16080903 - 20 Apr 2026
Cited by 1 | Viewed by 925
Abstract
Cadmium contamination threatens safe vegetable production in arable soils. This study evaluated whether soil-applied selenium (Se) could reduce Cd accumulation in Artemisia selengensis while keeping Se enrichment within a practical safety range. A field plot experiment was sampled at 75, 110, and 150 [...] Read more.
Cadmium contamination threatens safe vegetable production in arable soils. This study evaluated whether soil-applied selenium (Se) could reduce Cd accumulation in Artemisia selengensis while keeping Se enrichment within a practical safety range. A field plot experiment was sampled at 75, 110, and 150 d, and two pot experiments were conducted under normal and elevated Cd backgrounds. Six sodium selenite rates (0.5–8.0 mg kg−1; Se1–Se6) plus a control were applied. In the field experiment, Se5–Se6 (6.5–8.0 mg kg−1) reduced stem Cd by 33.0–39.3% at 75 d and 34.3–36.5% at 110 d, but the reduction declined to 24.8% at 150 d. Se application increased tissue Se (leaf > stem > rhizome), while stem total Se remained within the dry-weight reference window under 12Se–Se (0.5–2.0 mg kg−1). Se5–Se6 also increased soil pH and reduced bioavailable Cd by 8.8–10.2%, whereas stem Cd reduction under an elevated Cd background was limited and non-significant. Overall, 0.5–2.0 mg kg−1 (Se1–Se2) provided a practical window for Cd mitigation, while 6.5–8.0 mg kg−1 (Se5–Se6) increased the risk of excessive Se accumulation in edible tissues. Full article
(This article belongs to the Topic Recent Advances in Soil Health Management)
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18 pages, 1716 KB  
Article
Development and Tiller Formation in Wild and Domesticated Accessions of Timothy (Phleum pratense) and Its Relatives P. nodosum and P. alpinum
by Yousef Rahimi, Girma Bedada, Anne-Maj Gustavsson, Pär K. Ingvarsson, Per-Olof Lundquist and Anna Westerbergh
Agriculture 2026, 16(8), 902; https://doi.org/10.3390/agriculture16080902 - 19 Apr 2026
Viewed by 742
Abstract
The perennial grass timothy (Phleum pratense) is an important forage crop in cold temperate regions. It forms three types of tillers: vegetative (VEG), generative (GEN), and non-flowering elongated (ELONG). To understand the influence of plant development and tiller formation on biomass [...] Read more.
The perennial grass timothy (Phleum pratense) is an important forage crop in cold temperate regions. It forms three types of tillers: vegetative (VEG), generative (GEN), and non-flowering elongated (ELONG). To understand the influence of plant development and tiller formation on biomass production and the diversity in these traits, a total of 246 wild and domesticated accessions of timothy and the related species, P. nodosum and P. alpinum, were investigated. The length of different plant developmental stages and the formation of different tiller types were studied to test the hypotheses: (1) the proportion (%) of different tiller types affects biomass and is influenced by the lengths of the different plant developmental stages, (2) domestication and breeding have affected the length of developmental stages and proportions of tiller types. While timothy cultivars did not differ significantly from wild accessions in biomass, wild accessions had higher VEG%, which increased with latitude of accession origin. P. nodosum cultivars produced the highest number of ELONG of all accessions and species, and the ELONG% showed a strong positive correlation with biomass. Timothy cultivars showed later emergence and tillering, and reached stem elongation and heading earlier than wild accessions, suggesting that delayed tillering, but an overall faster development, has been favoured during breeding. The time between tillering and stem elongation showed a positive correlation with VEG%. This study reveals large diversity in developmental and tiller traits among accessions, reflecting differences in their domestication and breeding history, and highlighting the importance of considering early developmental traits and ELONG formation for yield and quality in further pre-breeding research. Full article
(This article belongs to the Special Issue Forage Breeding and Cultivation—2nd Edition)
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39 pages, 7225 KB  
Article
Enhancing Agri-Food Supply Chain Resilience: A FIT2 Gaussian Fuzzy FUCOM-QFD Framework for Designing Sustainable Controlled-Environment Hydroponic Agriculture Systems
by Biset Toprak and A. Çağrı Tolga
Agriculture 2026, 16(8), 901; https://doi.org/10.3390/agriculture16080901 - 19 Apr 2026
Viewed by 937
Abstract
Vulnerabilities in conventional agri-food supply chains (CAFSCs) necessitate a shift toward resilient, localized production models. Within the Agri-Food 4.0 landscape, urban Controlled-Environment Hydroponic Agriculture (CEHA) systems address these challenges by shortening supply chains and mitigating climate-induced breakdowns. However, structurally aligning Triple Bottom Line [...] Read more.
Vulnerabilities in conventional agri-food supply chains (CAFSCs) necessitate a shift toward resilient, localized production models. Within the Agri-Food 4.0 landscape, urban Controlled-Environment Hydroponic Agriculture (CEHA) systems address these challenges by shortening supply chains and mitigating climate-induced breakdowns. However, structurally aligning Triple Bottom Line (TBL)-oriented stakeholder needs with complex technical specifications remains a critical challenge in sustainable CEHA system design. To address this challenge, the present study proposes a novel framework integrating the Full Consistency Method (FUCOM) and Quality Function Deployment (QFD) within a Finite Interval Type-2 (FIT2) Gaussian fuzzy environment. This approach systematically translates TBL-oriented priorities into precise engineering specifications, mapping 17 stakeholder needs (SNs) to 30 technical design requirements (TDRs) while capturing linguistic uncertainty and hesitation. The findings reveal a clear strategic focus on environmental and social sustainability. Specifically, high product quality, food safety and traceability, consumer acceptance, and minimization of environmental impacts emerge as the primary drivers of CEHA adoption. The QFD translation identifies scalable IoT infrastructure, sensor maintenance and calibration, and AI-enabled decision support as the most critical TDRs. The framework’s reliability and structural robustness were rigorously validated through comprehensive analyses, including Kendall’s W test to confirm expert consensus, alongside a Leave-One-Out (LOO) approach, weight perturbations, and a structural evaluation of TDR intercorrelations. These findings provide a scientifically grounded roadmap for designing sustainable, intelligent urban agricultural systems. Ultimately, this framework offers actionable managerial implications for agribusiness stakeholders to bridge strategic TBL-oriented goals with practical engineering, significantly enhancing agri-food supply chain resilience. Full article
(This article belongs to the Special Issue Building Resilience Through Sustainable Agri-Food Supply Chains)
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16 pages, 4563 KB  
Article
Characterizing the Fusarium incarnatum-equiseti Species Complex Associated with Muskmelon Wilt and Evaluating the Biocontrol Potential of Bacillus subtilis MCLB2
by Jui-Hsin Chang, Yu-Hsuan Chen, Jenn-Wen Huang and Tzu-Pi Huang
Agriculture 2026, 16(8), 900; https://doi.org/10.3390/agriculture16080900 - 18 Apr 2026
Viewed by 779
Abstract
Muskmelon (Cucumis melo L.) is an economically important crop that remains highly susceptible to destructive fungal diseases, including gummy stem blight, downy mildew, Fusarium wilt, and anthracnose. Although fungicides and resistant cultivars are widely used, reliance on chemical control raises concerns regarding [...] Read more.
Muskmelon (Cucumis melo L.) is an economically important crop that remains highly susceptible to destructive fungal diseases, including gummy stem blight, downy mildew, Fusarium wilt, and anthracnose. Although fungicides and resistant cultivars are widely used, reliance on chemical control raises concerns regarding environmental safety, food quality, and the emergence of fungicide-resistant pathogen populations. Consequently, microbial biopesticides, particularly Bacillus species, have attracted increasing attention as sustainable alternatives. In this study, muskmelon plants exhibiting leaf wilting, chlorosis, and stem yellowing were collected from Guangming Farm in Wufeng, Taichung, Taiwan, and associated pathogens were isolated from stem tissues and identified to determine the causal agent of these symptoms. In addition, the biocontrol efficacy of Bacillus subtilis strain MCLB2 against melon fruit rot, as well as its underlying mechanisms, was evaluated. Pathogenicity assays confirmed that isolate F01 was the causal agent. Based on morphological characteristics and internal transcribed spacer (ITS) sequence analysis, this isolate showed 99.8% identity to Fusarium pernambucanum URM 7559 (GenBank accession no. NR_163754), and phylogenetic analysis further placed it within the Fusarium incarnatum-equiseti species complex (FIESC). Antagonistic assays demonstrated that B. subtilis MCLB2 significantly inhibited mycelial growth and suppressed the spore germination of F. pernambucanum. In addition, culture filtrates of strain MCLB2 effectively reduced Fusarium-induced fruit rot in melon and disrupted fungal cellular respiration. Liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis revealed that the strain produced surfactin-family lipopeptides. In conclusion, B. subtilis MCLB2 exhibits potential as a sustainable biocontrol agent for managing Fusarium fruit rot in melon, likely through surfactin-mediated disruption of fungal cellular respiration. Full article
(This article belongs to the Special Issue Biocontrol Agents for Plant Pest Management)
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33 pages, 1232 KB  
Review
Closing the Loop in Plant-Based Food Systems: Polyphenol Recovery from Agro-Food Chain By-Products
by Andor Paul, Maria Simona Chiș, Adriana Păucean, Anca Corina Fărcas, Purificacion Garcia-Segovia, Monica Negrea, Daniela Voica, Simona Nicoleta Oros and Maria Beatriz Prior Pinto Oliveira
Agriculture 2026, 16(8), 899; https://doi.org/10.3390/agriculture16080899 - 18 Apr 2026
Cited by 4 | Viewed by 1226
Abstract
The exponential growth of the fruit-processing industry generates significant quantities of organic by-products, such as peels, seeds, and pomace, which represent a rich but underutilized source of bioactive polyphenols. Valorizing these residues is critical for the transition toward a circular bioeconomy, yet conventional [...] Read more.
The exponential growth of the fruit-processing industry generates significant quantities of organic by-products, such as peels, seeds, and pomace, which represent a rich but underutilized source of bioactive polyphenols. Valorizing these residues is critical for the transition toward a circular bioeconomy, yet conventional extraction methods remain solvent-intensive and kinetically inefficient. This review provides a comprehensive analysis of emerging green extraction technologies, specifically Ultrasound-Assisted (UAE), Microwave-Assisted (MAE), Enzyme-Assisted (EAE), Pressurized Liquid (PLE), and Supercritical Fluid Extraction (SFE), and Pulsed Electric Field (PEF), applied to key industrial matrices including apple, citrus, grape, olive, and coffee. Comparative data demonstrate that intensification technologies significantly outperform conventional maceration, with UAE and MAE reducing processing times by up to 90% while enhancing polyphenol yields by 20–55% through mechanisms such as acoustic cavitation and dipole rotation. Furthermore, high-pressure methods exhibit tunable selectivity, enabling the specific recovery of heat-sensitive anthocyanins and bound phenolics without the use of toxic organic solvents. The study concludes that the future of industrial valorization lies in the adoption of hybrid technologies and sequential biorefinery strategies to achieve high-purity isolates with minimal environmental impact. Full article
(This article belongs to the Special Issue Innovative Approaches to Circular Agriculture: Incorporating Green Chemistry and Waste Valorization)
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18 pages, 2195 KB  
Article
Divergent Microbial and Enzymatic Drivers Regulate Particulate and Mineral-Associated Organic Carbon During Alpine Meadow Restoration
by Guanghua Jing, Mengmeng Wen, Xue Zhao, Wanyu He, Fazhu Zhao, Jun Wang and Sha Zhou
Agriculture 2026, 16(8), 898; https://doi.org/10.3390/agriculture16080898 - 18 Apr 2026
Viewed by 842
Abstract
Particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) are two operationally defined fractions frequently used in studies related to soil organic carbon (SOC) dynamics. However, the changes and governing mechanisms of these fractions, particularly along a restoration chronosequence, remain poorly understood. Here, [...] Read more.
Particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) are two operationally defined fractions frequently used in studies related to soil organic carbon (SOC) dynamics. However, the changes and governing mechanisms of these fractions, particularly along a restoration chronosequence, remain poorly understood. Here, we investigated changes in SOC fractions, soil properties, and microbial communities across a restoration chronosequence (1, 5, 7, 13, and 20 years) of alpine meadows using a space-for-time substitution approach on the Qinghai–Tibet Plateau. We quantified the contributions of biotic and abiotic drivers using Spearman correlation analysis, linear regression and random forest analysis. The results revealed a unimodal pattern in SOC, POC, and MAOC contents, peaking at 7, 5, and 7 years, respectively, with no further increase thereafter. Restoration duration strongly shaped microbial community structure and observed species richness, but had no significant effect on Shannon index and Pielou index. Random forest analysis identified soil water content (SWC) and total nitrogen (TN) as the primary predictors of SOC. The microbial community composition dominated the variation in POC while enzyme activity was the key driver of MAOC. Our findings highlight that soil carbon accumulation during alpine meadow restoration is a nonlinear process with a temporal threshold, and POC and MAOC are regulated by distinct biotic and abiotic mechanisms. This study provides a theoretical basis for understanding carbon sequestration mechanisms during alpine meadow restoration and developing sustainable grassland management strategies. Full article
(This article belongs to the Section Agricultural Soils)
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28 pages, 4881 KB  
Systematic Review
Research on Soil Acidification and Heavy Metals: A Comparative Bibliometric Analysis Based on CNKI and Web of Science (2005–2025)
by Lu Wang, Haisheng Cai, Jianfu Wu, Xueling Zhang, Zhihong Lu, Taifeng Zhu, Chenglong Yu, Xiong Fang, Peng Xiong and Ke Liu
Agriculture 2026, 16(8), 897; https://doi.org/10.3390/agriculture16080897 - 17 Apr 2026
Cited by 1 | Viewed by 1128
Abstract
The synergistic effects of soil acidification and heavy metal pollution present major challenges for global agroecosystems. To systematically trace the evolution of research and identify key topics in this field, this study employed CiteSpace to visualize and analyze 691 records from the China [...] Read more.
The synergistic effects of soil acidification and heavy metal pollution present major challenges for global agroecosystems. To systematically trace the evolution of research and identify key topics in this field, this study employed CiteSpace to visualize and analyze 691 records from the China National Knowledge Infrastructure (CNKI) and 6747 highly relevant articles or reviews from the Web of Science (WOS) Core Collection database from 2005 to 2025. The results indicate a steady to rapid rise in global publications, with China contributing the largest share, at 2468 publications. This has produced a research cluster centered around the Chinese Academy of Sciences (CAS); however, the centrality of its international cooperation remains limited. Studies in the CNKI database are driven by agricultural needs, focusing on national food security, rice yield stability, improvement of arable land, and heavy metal passivation and remediation, with a concentration on basic agricultural science. By contrast, research in the WOS database emphasizes fundamental mechanisms and interdisciplinary integration, addressing aluminum toxicity, microbial communities, the nitrogen cycle, and global climate change, intersecting fields such as environmental science, soil science, ecology, and microbiology. The evolution of research hotspots shows a clear trajectory: from acidity regulation and chemical speciation analysis of heavy metals (2005–2013), to heavy metal passivation, remediation, and phytoremediation (2014–2018), and then to biochar materials, microbiome analysis, and the synergistic role of carbon sequestration (2019–2025). This study argues that future research should move beyond single remediation measures and adopt integrated strategic management to jointly improve bioremediation efficiency, promote soil carbon sequestration and soil health, and enhance microbial adaptation to global climate change. Full article
(This article belongs to the Section Agricultural Soils)
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23 pages, 48382 KB  
Article
Spatial Distribution and System Constraints Diagnosis of Medium- and Low-Yield Farmlands in Northern China Based on Remote Sensing
by Xiangyang Sun, Zhenlin Tian, Zhanqing Zhao, Yuping Lei, Wenxu Dong, Chunsheng Hu, Chaobo Zhang and Xiuping Liu
Agriculture 2026, 16(8), 896; https://doi.org/10.3390/agriculture16080896 - 17 Apr 2026
Viewed by 580
Abstract
Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale [...] Read more.
Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale MLYF classification and system constraints diagnosis. To address the current methodological gaps, this study developed a comprehensive framework to determine the spatial distribution of MLYF in northern China and clarify their key constraints. The framework combined the Spatio-Temporal Random Forest (STRF) algorithm with vegetation indices (VIs), climate, and soil data to delineate MLYF and uses interpretable machine learning to diagnose major constraints. The model showed high explanatory power and ensured the reliability of attribution results. The results showed that MLYF exhibited obvious spatial heterogeneity, accounting for 48.66% of the total cultivated land in the study area. These MLYF are primarily concentrated in the northwestern Loess Plateau (LP), the central Along the Great Wall (ATGW) region, and the peripheries of the Huang-Huai-Hai (HHH) Plain. In addition to spatial classification, our analysis revealed significant differences in constraint mechanisms: soil structural, nutrient, and salinization constraints predominantly restrict productivity in the HHH Plain, whereas water stress and soil erosion are the primary drivers of yield gaps in the LP and ATGW regions. These findings provide new data and insights for understanding the spatial heterogeneity of farmland quality in typical dryland agricultural regions in northern China, and offer a scientific basis for targeted land improvement and regional agricultural sustainability. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Arable Crops Monitoring and Yield Assessment)
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16 pages, 5011 KB  
Article
Identification and Functional Characterization of Nine Glutathione S-Transferase Genes in Lasioderma serricorne Reveals Their Roles in Detoxification of Emerging Fumigants
by Mingxun Zu, Yu Shen, Kangkang Xu, Qian Guo, Wenjia Yang, Guy Smagghe and Can Li
Agriculture 2026, 16(8), 895; https://doi.org/10.3390/agriculture16080895 - 17 Apr 2026
Viewed by 683
Abstract
The cigarette beetle, Lasioderma serricorne, is a globally important pest of stored products, and prolonged fumigant use has accelerated resistance development. Glutathione S-transferases (GSTs) are key phase II detoxification enzymes that mediate insect tolerance to xenobiotics. In this study, we identified [...] Read more.
The cigarette beetle, Lasioderma serricorne, is a globally important pest of stored products, and prolonged fumigant use has accelerated resistance development. Glutathione S-transferases (GSTs) are key phase II detoxification enzymes that mediate insect tolerance to xenobiotics. In this study, we identified nine GST genes (LsGSTs) in L. serricorne and classified them into four cytosolic classes, namely epsilon, delta, theta, and sigma, based on phylogenetic analysis. Most LsGSTs were predominantly expressed during larval stages, while LsGSTs7 showed peak expression in adults. Tissue-specific profiling revealed predominant expression in metabolically active organs, including the fat body, Malpighian tubules, and midgut. Inhibition of GST activity using diethyl maleate (DEM) significantly increased larval susceptibility to three emerging fumigants: ethyl formate, benzothiazole, and methyl isothiocyanate. Exposure to LC30 and LC50 concentrations of these fumigants induced up-regulation of multiple LsGSTs, highlighting fumigant-specific detoxification responses. RNA interference targeting nine fumigant-inducible LsGSTs markedly elevated mortality and decreased total GST activity under fumigant stress. Furthermore, recombinant LsGSTs6 protein effectively metabolized methyl isothiocyanate, confirming their direct role in fumigant detoxification. Collectively, these findings provide novel insights into the molecular mechanisms underlying GST-mediated tolerance in L. serricorne and identify specific GST isoenzymes as promising molecular targets for innovative resistance management strategies in stored-product pest control. Full article
(This article belongs to the Special Issue Sustainable Use of Pesticides—2nd Edition)
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18 pages, 1586 KB  
Article
Longevity of Red and Holstein Breed Dairy Cows in Latvia
by Lasma Cielava, Liga Paura and Daina Jonkus
Agriculture 2026, 16(8), 894; https://doi.org/10.3390/agriculture16080894 - 17 Apr 2026
Viewed by 616
Abstract
Longevity is a key determinant of economic efficiency and sustainability in dairy cattle production. The study evaluated longevity and lifetime productivity traits in red breed (RB) and Holstein breed (HB) dairy cows in Latvia and assessed the effects of birth year, age at [...] Read more.
Longevity is a key determinant of economic efficiency and sustainability in dairy cattle production. The study evaluated longevity and lifetime productivity traits in red breed (RB) and Holstein breed (HB) dairy cows in Latvia and assessed the effects of birth year, age at first calving (AFC), and farm size. Data from 279,818 culled and living cows born between 2010 and 2019 were analyzed. The aim of the study was to investigate biological and farm-level factors affecting dairy cow longevity in RB and HB populations. RB and HB cows born between 2010 and 2013 had significantly longer (p < 0.05) total lifespan (LS) and productive life (PL) than cows born in later years; however, they showed lower life day (MPD) and productive life day (MPPD) milk productivity. AFC significantly affected MPD and MPPD. The highest life day productivity in RB cows was observed at AFC of 22.1–24 months, while in HB cows it was observed at <24 months. Significantly longer LS and PL were recorded on farms with fewer than 20 cows, whereas the highest MPD was observed on farms with 101–500 cows in RB and 101–200 cows in HB. Full article
(This article belongs to the Special Issue Quantitative Genetics of Livestock Populations)
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35 pages, 8415 KB  
Article
Research on Three-Dimensional Positioning Method for Automatic Strawberry Fruit Picking Based on Vision–IMU Fusion
by Bowen Liu, Chuhan Chen, Junqiu Li, Qinghui Zhang and Yinghao Meng
Agriculture 2026, 16(8), 893; https://doi.org/10.3390/agriculture16080893 - 17 Apr 2026
Cited by 1 | Viewed by 1398
Abstract
Accurate fruit localization and efficient harvesting are key challenges for agricultural robots, especially in dynamic orchard environments, where platform vibration, fruit occlusion, and computational resource limitations of embedded devices significantly impact system performance. To address these issues, this paper proposes a lightweight “fruit [...] Read more.
Accurate fruit localization and efficient harvesting are key challenges for agricultural robots, especially in dynamic orchard environments, where platform vibration, fruit occlusion, and computational resource limitations of embedded devices significantly impact system performance. To address these issues, this paper proposes a lightweight “fruit detection + harvesting” framework. First, by integrating MobileNetV4 and Triplet Attention mechanisms, an improved YOLOv8n network is designed, with the improved YOLOv8n Precision reaching 98.148% and FPS reaching 30 FPS on Jetson Nano, achieving a good balance between detection accuracy and computational efficiency suitable for edge deployment. Second, a strawberry three-dimensional coordinate reconstruction method based on weighted 3D centroid reconstruction is proposed, utilizing depth bias adjustment coefficients to improve spatial accuracy. Third, to address localization errors caused by vibration and platform motion, a dynamic compensation and temporal fusion strategy based on an Inertial Measurement Unit (IMU) is proposed. The rotation matrix estimated from IMU data is first used to correct camera pose variations. Then, an adaptive sliding window is employed to smooth the coordinate sequence. Finally, an Extended Kalman Filter (EKF) is applied to further refine the fused results by incorporating temporal dynamics, ensuring that the reconstructed three-dimensional coordinates in the robotic arm reference frame achieve higher stability and continuity. Experimental results in orchard scenarios show that compared with traditional methods, the system has higher localization accuracy, stronger robustness to dynamic disturbances, and higher harvesting efficiency. This work provides a practical and deployable solution for advancing intelligent fruit-harvesting robots. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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19 pages, 1151 KB  
Article
Policy-Relevant Insights into Household Biogas Adoption in West Java, Indonesia: Evidence from a Logistic Regression Analysis
by Ricardo Situmeang, Jana Mazancová and Hynek Roubík
Agriculture 2026, 16(8), 892; https://doi.org/10.3390/agriculture16080892 - 17 Apr 2026
Cited by 2 | Viewed by 707
Abstract
This study examines the determinants of household biogas adoption among dairy farmers in West Java, Indonesia. Despite ongoing government efforts to promote renewable energy, adoption remains limited and often not sustainable. Using cross-sectional survey data from biogas users and non-users, a binary logistic [...] Read more.
This study examines the determinants of household biogas adoption among dairy farmers in West Java, Indonesia. Despite ongoing government efforts to promote renewable energy, adoption remains limited and often not sustainable. Using cross-sectional survey data from biogas users and non-users, a binary logistic regression model was applied to assess the role of socioeconomic characteristics, resource availability, and institutional support. The results indicate that perceived time savings, fuel-cost pressure, and participation in training programs are significant positive drivers of adoption. In contrast, higher levels of formal education and livestock ownership are associated with a lower likelihood of adoption, suggesting that resource availability alone does not guarantee use. Overall, the findings highlight that adoption depends not only on economic factors but also on how well biogas systems align with household practices and capabilities. The study provides policy-relevant insights for improving the design and implementation of biogas programs in rural Indonesia. Full article
(This article belongs to the Special Issue Sustainability and Energy Economics in Agriculture—2nd Edition)
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18 pages, 4957 KB  
Article
Calibration of DEM Contact Parameters for High-Moisture Rabbit Manure Using the Hertz–Mindlin with a JKR Model and a Three-Stage Optimization Strategy
by Zhihang Cui, Min Zhou, Xun Suo and Zichen Yang
Agriculture 2026, 16(8), 891; https://doi.org/10.3390/agriculture16080891 - 17 Apr 2026
Viewed by 595
Abstract
Rabbit manure with high-moisture content exhibits complex adhesive and flow behaviors, which make accurate parameterization in discrete element method (DEM) simulations difficult. To improve the reliability of DEM modeling for rabbit manure composting processes, this study calibrated the contact parameters of rabbit manure [...] Read more.
Rabbit manure with high-moisture content exhibits complex adhesive and flow behaviors, which make accurate parameterization in discrete element method (DEM) simulations difficult. To improve the reliability of DEM modeling for rabbit manure composting processes, this study calibrated the contact parameters of rabbit manure at 65% moisture content using the angle of repose as the target response. A physical angle of repose test was first conducted using the cylindrical lifting method, yielding a measured value of 38.77°. The Hertz–Mindlin with Johnson–Kendall–Roberts (JKR) contact model was then adopted to represent the adhesive behavior of the material, and a three-stage optimization strategy consisting of a Plackett–Burman screening test, a steepest ascent test, and a Box–Behnken design was applied to identify and optimize the key parameters. The results showed that the particle restitution coefficient, rabbit manure–PLA rolling friction coefficient, and surface energy were the dominant factors affecting the angle of repose. The optimal parameter combination was a particle restitution coefficient of 0.56, a rabbit manure–PLA rolling friction coefficient of 0.375, and a surface energy of 0.243 J/m2. Under these conditions, the simulated angle of repose was 39.21°, with a relative error of 1.13%. These calibrated parameters provide a reliable basis for DEM simulation and engineering optimization of rabbit manure composting equipment. Full article
(This article belongs to the Section Agricultural Technology)
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20 pages, 2977 KB  
Article
Predicting AquaCrop-Simulated Durum Wheat Yield with Machine Learning: Algorithm Comparison and Agronomic Signal Convergence in the Capitanata Plain
by Pasquale Garofalo, Anna Rita Bernadette Cammerino and Maria Riccardi
Agriculture 2026, 16(8), 890; https://doi.org/10.3390/agriculture16080890 - 17 Apr 2026
Cited by 1 | Viewed by 700
Abstract
Durum wheat production in the Mediterranean basin faces increasing climate variability and thus the need for computationally efficient tools to support agronomic decision-making at regional scale. Process-based crop models such as AquaCrop provide mechanistically sound yield estimates but require substantial computation time when [...] Read more.
Durum wheat production in the Mediterranean basin faces increasing climate variability and thus the need for computationally efficient tools to support agronomic decision-making at regional scale. Process-based crop models such as AquaCrop provide mechanistically sound yield estimates but require substantial computation time when screening large numbers of soil–climate–management combinations. The present study addresses this constraint by developing and evaluating five machine learning (ML) surrogate models—Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine for regression (SMOreg), RandomTree, and Reduced Error Pruning Tree (REPTree)—trained to emulate the AquaCrop-GIS response surface for durum wheat (Triticum durum Desf.) grain yield across the Capitanata plain (Southern Italy). A dataset of 342 instances was constructed by crossing 25 soil profiles, three sowing dates, and two irrigation regimes across 15 climatic grid cells (2014–2023), evaluated by stratified 10-fold cross-validation. The MLP achieved the highest accuracy (R = 0.983; R2 = 0.966; RMSE = 0.083 t ha−1); the four interpretable models were clustered at R = 0.891–0.907 (RMSE = 0.192–0.203 t ha−1). All models converged on consistent agronomic signals: standard sowing (1 November) yielded +0.53 t ha−1 over late sowing (15 November), supplemental irrigation added +0.17 t ha−1, and fine-textured soils produced superior yields. The convergence of directional signals across linear, kernel-based, and tree-based architectures confirms that ML surrogates trained on process-model outputs can efficiently emulate AquaCrop response surfaces and deliver actionable management guidance for durum wheat producers and agricultural planners in Mediterranean dryland farming systems. Full article
(This article belongs to the Special Issue Cropland Suitability Evaluation Related to Crop Yield Based on Geospatial Data)
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15 pages, 853 KB  
Article
Comparative Economic Evaluation of Greenhouse Pepper Cultivation Under Conventional and Organic Management
by Begoña García Castellanos, Pedro Enrique Fuster Villa, José García García and Felipe Egea Clemente
Agriculture 2026, 16(8), 889; https://doi.org/10.3390/agriculture16080889 - 16 Apr 2026
Viewed by 1190
Abstract
Economic comparison of different production models, focusing on production costs and economic viability–profitability are essential to guide decision-making and enhance the competitiveness and resilience of the agri-food chain. This is the overall objective of the paper: to establish the production and cost structure [...] Read more.
Economic comparison of different production models, focusing on production costs and economic viability–profitability are essential to guide decision-making and enhance the competitiveness and resilience of the agri-food chain. This is the overall objective of the paper: to establish the production and cost structure and to economically evaluate two greenhouse pepper production models. Data on the production process of the production models (organic and conventional greenhouse pepper) and their cost and income structures were collected through 15 on-site surveys conducted with growers and horticultural sector technicians. The initial investment is very high (140,030 €·ha−1) and requires a significant financial effort for implementation, being the same for both conventional and organic cultivation, this means that fixed costs are high in percentage terms, especially when compared to other intensive outdoor horticultural crops. The key cost differences between the two systems are found in fertilisers, plant protection treatments, and biotechnological control and cost structure indicates a higher unit cost for organically produced peppers, 0.60 €·kg−1 for conventional and 0.75 €·kg−1 for organic production. Greenhouse pepper cultivation in southeastern Spain is an economically viable and profitable activity under both conventional and organic management. Profitability indicators consistently show that the conventional system is more profitable at the same farm scale or size. Full article
(This article belongs to the Special Issue Farmer Behavior and Sustainable Agricultural Management)
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24 pages, 10975 KB  
Article
Cucumber Robotic Continuous Harvesting: Enhanced YOLOv8n Detection and Dynamic Bézier Curve-Assisted Collision-Free Path Generation
by Chengheng Zhao, Huan Wang, Wenhao Li, Hengyi Zheng, Le Zhou and Mengbo Qian
Agriculture 2026, 16(8), 888; https://doi.org/10.3390/agriculture16080888 - 16 Apr 2026
Cited by 1 | Viewed by 816
Abstract
To address the inefficiency of the long single-fruit grasping cycle in traditional fruit harvesting robots, this study proposes a collision-free continuous harvesting solution for cucumber cultivation scenarios, coupled with a customized robotic system equipped with a continuous harvesting end-effector. In terms of visual [...] Read more.
To address the inefficiency of the long single-fruit grasping cycle in traditional fruit harvesting robots, this study proposes a collision-free continuous harvesting solution for cucumber cultivation scenarios, coupled with a customized robotic system equipped with a continuous harvesting end-effector. In terms of visual perception, the YOLOv8n model is enhanced by integrating the GhostNet lightweight architecture, the Context-Guided Fusion Module (CGFM), and the MPDIoU loss function. Ablation experiments confirm the optimal model configuration, and the optimized model achieves a reduced model size of 5.3 MB and computational load of 6.6 GFLOPs while improving the mean average precision (mAP@50) by 2.5%, which facilitates low-cost deployment. For path planning, an Enhanced Bézier Continuous Picking (EBCP) algorithm is developed by combining 3D Gaussian kernel modeling and cubic Bézier curves to generate collision-free continuous trajectories. Simulation and practical experiments demonstrate that the path length of the proposed continuous picking method is only 31.1% that of the traditional path, with a theoretical collision-free rate of 96.69% and an actual collision-free rate of 92.24%. The feasibility and effectiveness of the proposed system are fully verified, providing a technical reference for the continuous operation of fruit harvesting robots. Full article
(This article belongs to the Section Agricultural Technology)
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18 pages, 3204 KB  
Article
Integrated Nutrient Management Enhances Root Growth, Nutrient Use Efficiency, and Ratooning Ability in Rice Under Acidic Paddy Soils
by Yuhu Lin, Weize Wang, Haoyan Zhang, Yaoyao Jiang, Xiaoman Wang, Yongjia Zhong and Hong Liao
Agriculture 2026, 16(8), 887; https://doi.org/10.3390/agriculture16080887 - 16 Apr 2026
Viewed by 787
Abstract
Ratoon rice is a unique cropping system that utilizes the regenerative capacity of rice tillers to achieve one sowing with two harvests in a single growing season, thus exhibiting great yield potential. However, the ratooning ability is often constrained by impaired root function [...] Read more.
Ratoon rice is a unique cropping system that utilizes the regenerative capacity of rice tillers to achieve one sowing with two harvests in a single growing season, thus exhibiting great yield potential. However, the ratooning ability is often constrained by impaired root function after the first harvest. In this study, we established an integrated nutrient management (INM) strategy to enhance root growth and function, thereby improving nutrient use efficiency and yield. Compared with farmers’ conventional management (FCM), INM increased annual total yield by 7.8% and 13.9% and enhanced ratooning ability by 20.7% and 19.0% in 2024 and 2025, respectively. INM consistently maintained higher root biomass in both main and ratoon crops: by 26.9% and 54.0% in 2024, and by 44.8% and 26.0% in 2025. Root biomass was significantly and positively correlated with brown rice weight across both seasons, and was positively associated with ratooning ability. INM also promoted early root establishment after transplanting, increasing the white-root number by 105.7%, 175.0%, and 484.8% at 3, 5, and 14 days after transplanting (DAT), respectively. Meanwhile, the xylem sap exudation rate and root triphenyl tetrazolium chloride (TTC) reduction activity were increased by 37.4% and 64.5% relative to FCM. In the 2024 ratoon season, INM improved nutrient use efficiency, with partial factor productivity (PFP) of nitrogen (PFPN), phosphorus (PFPP), and potassium (PFPK) increased by 371.0%, 59.3%, and 91.1%, respectively. Gene Set Enrichment Analysis (GSEA) revealed significant enrichment of gene sets involved in root growth, development, nutrient acquisition, and assimilation under INM, providing molecular evidence for root-mediated nutrient synergy. In summary, INM enhances root growth and function, promotes nutrient uptake and utilization, and consequently improves yield. These results offer a practical management strategy supported by physiological and transcriptomic evidence for boosting ratoon rice production via root-mediated nutrient synergies. Full article
(This article belongs to the Section Crop Production)
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18 pages, 3308 KB  
Article
Pyrolysis-Driven Trade-Offs Between Carbon Stabilization and Micronutrient Partitioning in Poultry Waste-Derived Biochars in Galicia (NW Spain)
by Pedro A. Garzón-Camacho, André Fischer Sbrissia, Antonio Paz-González, Vanessa Álvarez-López and Eliana Cárdenas-Aguiar
Agriculture 2026, 16(8), 886; https://doi.org/10.3390/agriculture16080886 - 16 Apr 2026
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Abstract
The conversion of livestock manure, including poultry waste (PW), into biochar represents a sustainable strategy to recycle nutrients while reducing environmental risks. This study evaluated how pyrolysis temperature regulates physicochemical properties, carbon structure, and nutrient dynamics in biochars produced from PW. Raw PW [...] Read more.
The conversion of livestock manure, including poultry waste (PW), into biochar represents a sustainable strategy to recycle nutrients while reducing environmental risks. This study evaluated how pyrolysis temperature regulates physicochemical properties, carbon structure, and nutrient dynamics in biochars produced from PW. Raw PW and biochars generated at 300 and 600 °C were characterized through proximate and elemental analyses, Fourrier Transform Infrared spectroscopy (FTIR), soil nutrient assessment, and germination bioassays. A multivariate approach was used to analyze the experimental data sets. Increasing pyrolysis temperature significantly reduced biochar yield (83.62% to 64.36%), while promoting carbon condensation and mineral enrichment, as indicated by the decline in H/C ratio from 1.02 to 0.22 and the increase in ash content from 41.47% to 56.77%. FTIR analysis revealed a progressive attenuation of O–H and aliphatic C–H functional groups and a relative increase in aromatic structures with increasing temperature, indicating structural reorganization of the carbon matrix. Total concentrations of macro- and micronutrients generally increased with temperature; for example, total Cu increased from 78.62 to 114.17 mg kg−1, while Zn increased from 557.03 to 819.66 mg kg−1 between 300 and 600 °C. In contrast, the bioavailable fractions of Fe, Cu, and Zn determined using the chelating agent DTPA declined, although not significantly (p < 0.05), with increasing pyrolysis temperature. Principal component analysis clearly distinguished raw PW from pyrolyzed materials, confirming pyrolysis temperature as the main factor dictating biochar properties. PW exhibited severe phytotoxicity, which was partially mitigated with increasing pyrolysis temperature. Overall, pyrolysis enhanced carbon stabilization and micronutrient immobilization, highlighting PW-derived biochars as promising soil amendments for improving nutrient management and reducing the environmental risks associated with raw PW application. Full article
(This article belongs to the Section Agricultural Technology)
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21 pages, 6281 KB  
Article
Study on the Group Threshing Characteristics of Maize Ear Kernels
by Xinping Li, Ruizhe Sun, Bin Peng, Yanan Li, Fuli Ma, Jing Pang, Lingxin Geng, Hongjian Wu, Jialiang Zhang, Junyi Wang and Mingyuan Wang
Agriculture 2026, 16(8), 885; https://doi.org/10.3390/agriculture16080885 - 16 Apr 2026
Viewed by 537
Abstract
To address the lack of direct experimental characterization of multi-kernel group threshing during maize ear threshing, an experimental study on maize ear group threshing was conducted based on kernel arrangement unit characteristics. By constructing a torque testing system for maize ear detachment, we [...] Read more.
To address the lack of direct experimental characterization of multi-kernel group threshing during maize ear threshing, an experimental study on maize ear group threshing was conducted based on kernel arrangement unit characteristics. By constructing a torque testing system for maize ear detachment, we analyzed the temporal variation in torque during detachment and its response to different experimental conditions. Statistical evaluation of torque variability and stability was performed using analysis of variance (ANOVA) and error bars. Furthermore, high-speed photography was employed to capture continuous images and analyze the trajectories of kernel motion during critical detachment stages, revealing the movement characteristics and shedding behavior of kernel clusters. The results indicate that the maize ear threshing process does not involve individual kernel detachment but primarily manifests as group threshing behavior with the arrangement unit as the fundamental unit. Furthermore, the characteristics of the variation in threshing torque correspond to the collective detachment process of the kernel group. This study provides direct experimental evidence for the group threshing mechanism of maize ears through both torque statistical analysis and high-speed visualization. These findings offer valuable insights for threshing mechanism research and the optimization of threshing components. Full article
(This article belongs to the Section Agricultural Technology)
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