Agriculture

25 pages, 841 KB

Open AccessArticle

The Impact of Supply Chain Finance on the Total Factor Productivity of Agricultural Enterprises: Evidence from China

by Haoyang Luo, Yue Yu, Lan Wang, Yanru Wu and Yan Liu

Agriculture 2025, 15(12), 1325; https://doi.org/10.3390/agriculture15121325 - 19 Jun 2025

Viewed by 650

As the primary force driving the sustainable development of the rural economy, the improvement of the total factor productivity (TFP) of agricultural enterprises (AEs) is of great strategic significance. This study innovatively zeroes in on AEs, leveraging micro-level data from agricultural listed companies [...] Read more.

As the primary force driving the sustainable development of the rural economy, the improvement of the total factor productivity (TFP) of agricultural enterprises (AEs) is of great strategic significance. This study innovatively zeroes in on AEs, leveraging micro-level data from agricultural listed companies in China’s A-share market spanning from 2007 to 2023. It aims to investigate the impact of supply chain finance (SCF) on the TFP of these enterprises and elucidate the underlying mechanisms. Uniquely, this study incorporates enterprise digital transformation and innovation capability as moderating variables into the mechanism analysis framework. Furthermore, it examines the heterogeneous effects across different characteristics of AEs. The findings reveal that SCF significantly boosts the TFP of AEs. Specifically, a one-standard-deviation increase in the level of SCF is associated with a 0.2658% increase in TFP relative to the mean. This conclusion holds robustly across various tests. Moreover, the interaction terms of SCF with both enterprise digital transformation and innovation capability are significantly positive. This indicates that greater digital transformation and stronger innovation capability amplify the positive effect of SCF on TFP. The heterogeneous analysis further indicates that for AEs with highly optimized human capital, higher financing constraints, and more efficient credit resource allocation, the positive impact of SCF on TFP is particularly pronounced. Full article

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

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25 pages, 630 KB

Open AccessArticle

The Impact of Global Digital Trade Development on China’s Grain Import Trade Potential: An Empirical Analysis Based on a Time-Varying Stochastic Frontier Gravity Model

by Dongpu Xu, Chunjie Qi, Guozhu Fang and Yumeng Gu

Agriculture 2025, 15(12), 1324; https://doi.org/10.3390/agriculture15121324 - 19 Jun 2025

Viewed by 499

Abstract

It is of great significance to clarify the impact of the rapid development of digital trade on China’s grain imports in order to enhance its efficiency and guarantee food security. From an import perspective, this article adopts a stochastic frontier gravity model and [...] Read more.

It is of great significance to clarify the impact of the rapid development of digital trade on China’s grain imports in order to enhance its efficiency and guarantee food security. From an import perspective, this article adopts a stochastic frontier gravity model and a trade inefficiency model to analyze the influence of global digital trade development on the efficiency of China’s grain imports and further estimates the potential for trade expansion. The main findings include the following: (a) Divergence in digital trade capabilities persists across nations. As countries advance their digital trade ecosystems, China’s grain import efficiency demonstrates corresponding enhancements. (b) Compared with digital infrastructure construction and digital trade competition intensity, China’s food import trade efficiency increases as the level of digital technology innovation improves. (c) China achieves the highest trade efficiency in grain import among the ASEAN (Association of Southeast Asian Nations) and North American countries, while the greatest untapped potential lies in imports from South America. Accordingly, for different countries, it is necessary to adopt different strategies to enhance cooperation with the world’s major grain-trading countries in the areas of digital trade infrastructure construction and digital technology innovation, and to use digital trade to optimize China’s grain import trade chain and improve its efficiency. Full article

(This article belongs to the Special Issue Productivity and Efficiency of Agricultural and Livestock Systems)

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26 pages, 3332 KB

Open AccessArticle

Dependence of the Abundance of Reed Glass-Winged Cicadas (Pentastiridius leporinus (Linnaeus, 1761)) on Weather and Climate in the Upper Rhine Valley, Southwest Germany

by Sai Kiran Kakarla, Eric Schall, Anna Dettweiler, Jana Stohl, Elisabeth Glaser, Hannah Adam, Franziska Teubler, Joachim Ingwersen, Tilmann Sauer, Hans-Peter Piepho, Christian Lang and Thilo Streck

Agriculture 2025, 15(12), 1323; https://doi.org/10.3390/agriculture15121323 - 19 Jun 2025

Viewed by 658

Abstract

The planthopper Pentastiridius leporinus, commonly called reed glass-winged cicada, transmits the pathogens “Candidatus Arsenophonus phytopathogenicus” and “Candidatus Phytoplasma solani”, which are infesting sugar beet and, most recently, also potato in the Upper Rhine valley area of Germany. They cause the [...] Read more.

The planthopper Pentastiridius leporinus, commonly called reed glass-winged cicada, transmits the pathogens “Candidatus Arsenophonus phytopathogenicus” and “Candidatus Phytoplasma solani”, which are infesting sugar beet and, most recently, also potato in the Upper Rhine valley area of Germany. They cause the “Syndrome Basses Richesses” associated with reduced yield and sugar content in sugar beet, leading to substantial monetary losses to farmers in the region. No effective solutions exist currently. This study uses statistical models to understand to what extent the abundance of cicadas depends on climate regions during the vegetation period (April–October). We further investigated what influence temperature and precipitation have on the abundance of the cicadas in sugar beet fields. Furthermore, we investigated the possible impacts of future climate on cicada abundance. Also, 22 °C and 8 mm/day were found to be the optimal temperature and precipitation conditions for peak male cicada flight activity, while 28 °C and 8 mm/day were the optimum for females. By the end of the 21st century, daily male cicada abundance is projected to increase significantly under the worst-case high greenhouse gas emission scenario RCP8.5 (RCP-Representative Concentration Pathways), with confidence intervals suggesting a possible 5–15-fold increase compared to current levels. In contrast, under the low-emission scenario RCP2.6, male cicada populations are projected to be 60–70% lower than RCP8.5. An understanding of the influence of changing temperature and precipitation conditions is crucial for predicting the spread of this pest to different regions of Germany and other European countries. Full article

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

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21 pages, 4259 KB

Open AccessArticle

Assessing Climate Risk in Viticulture: A Localized Index for the Semi-Arid and Mediterranean Regions of Chile

by Katherine Cuevas-Zárate, Donna Cortez, Jorge Soto and Manuel Paneque

Agriculture 2025, 15(12), 1322; https://doi.org/10.3390/agriculture15121322 - 19 Jun 2025

Viewed by 831

Abstract

Viticulture contributes significantly to Chile’s exports and GDP. However, the development and productivity of grapevines is threatened by climate change. Grapevines are grown in diverse regions; thus, adaptable tools for evaluating climate risk at the local level are required. In this study, a [...] Read more.

Viticulture contributes significantly to Chile’s exports and GDP. However, the development and productivity of grapevines is threatened by climate change. Grapevines are grown in diverse regions; thus, adaptable tools for evaluating climate risk at the local level are required. In this study, a local climate risk index (LCRI) was developed to assess the vulnerability of Chilean viticulture (wine, table, and pisco grapes) in the current (2017–2024) and future (2046–2065) periods. Various components, including exposure, sensitivity, and adaptive and response capacities, were analyzed using different indicators based on municipal-level information. The results for the current period indicated that most municipalities were at medium risk, whereas future projections showed a marked increase in climate risk, principally due to changes in climate suitability. In the current period, the highest LCRI values were observed in semi-arid and mediterranean zones, particularly in the northern regions of Atacama and Coquimbo; in the future period, this situation intensified. In contrast, the lowest values in the current period occurred in the Maule region and further south, where the climate transitions from mediterranean to temperate conditions, and in the future period, valley and mountainous areas presented improvements in the index. Some municipalities showed improvement or stability with local adaptation efforts. The results highlight the urgent need for region-specific adaptation policies that prioritize water management, infrastructure, and increased capacities. Full article

(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

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25 pages, 6846 KB

Open AccessArticle

DGA-ACO: Enhanced Dynamic Genetic Algorithm—Ant Colony Optimization Path Planning for Agribots

by Zhenpeng Zhang, Pengyu Li, Shanglei Chai, Yukang Cui and Yibin Tian

Agriculture 2025, 15(12), 1321; https://doi.org/10.3390/agriculture15121321 - 19 Jun 2025

Viewed by 562

Abstract

Recent advancements in agricultural mobile robots (agribots) have enabled the execution of critical tasks such as crop inspection, precision spraying, and selective harvesting. While agribots show significant potential, conventional path-planning algorithms suffer from three limitations: (1) inadequate dynamic obstacle avoidance, which may compromise [...] Read more.

Recent advancements in agricultural mobile robots (agribots) have enabled the execution of critical tasks such as crop inspection, precision spraying, and selective harvesting. While agribots show significant potential, conventional path-planning algorithms suffer from three limitations: (1) inadequate dynamic obstacle avoidance, which may compromise operational safety, (2) premature convergence to local optima, and (3) excessive energy consumption due to suboptimal trajectories. To overcome these challenges, this study proposes an enhanced Dynamic Genetic Algorithm—Ant Colony Optimization (DGA-ACO) framework. It integrates a 2D risk-penalty mapping model with dynamic obstacle avoidance mechanisms, improves max–min ant system pheromone allocation through adaptive crossover-mutation operators, and incorporates a hidden Markov model for accurately forecasting obstacle trajectories. A multi-objective fitness function simultaneously optimizes path length, energy efficiency, and safety metrics, while genetic operators prevent algorithmic stagnation. Simulations in different scenarios show that DGA-ACO outperforms Dijkstra, A*, genetic algorithm, ant colony optimization, and other state-of-the-art methods. It achieves shortened path lengths and improved motion smoothness while achieving a certain degree of dynamic obstacle avoidance in the global path-planning process. Full article

(This article belongs to the Special Issue Research Advances in Perception for Agricultural Robots)

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26 pages, 10157 KB

Open AccessArticle

Improving Soil Moisture Estimation by Integrating Remote Sensing Data into HYDRUS-1D Using an Ensemble Kalman Filter Approach

by Yule Sun, Quanming Liu, Chunjuan Wang, Qi Liu and Zhongyi Qu

Agriculture 2025, 15(12), 1320; https://doi.org/10.3390/agriculture15121320 - 19 Jun 2025

Viewed by 453

Abstract

Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework [...] Read more.

Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework that combines satellite-derived soil moisture estimates, ground-based observations, the HYDRUS-1D vadose zone model, and the ensemble Kalman filter (EnKF) data assimilation method to improve soil moisture simulations over saline-affected farmland in the Hetao irrigation district. Vegetation effects were first removed using the water cloud model; after correction, a cubic regression using the vertical transmit/vertical receive (VV) signal retrieved surface moisture with an R² value of 0.7964 and a root mean square error (RMSE) of 0.021 cm³·cm⁻³. HYDRUS-1D, calibrated against multi-depth field data (0–80 cm), reproduced soil moisture profiles at 17 sites with RMSEs of 0.017–0.056 cm³·cm⁻³. The EnKF assimilation of satellite and ground observations further reduced the errors to 0.008–0.017 cm³·cm⁻³, with the greatest improvement in the 0–20 cm layer; the accuracy declined slightly with depth but remained superior to either data source alone. Our study improves soil moisture simulation accuracy and closes the knowledge gaps in multi-source data integration. This framework supports sustainable land management and irrigation policy in vulnerable farming regions. Full article

(This article belongs to the Special Issue Model-Based Evaluation of Crop Agronomic Traits)

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14 pages, 3355 KB

Open AccessArticle

Establishment and Application of Loop-Mediated Isothermal Amplification Assays for Pathogens of Rice Bakanae Disease

by Xinchun Liu, Yan Wang, Yating Zhang, Jingzhao Xia, Chenxi Liu, Yu Song, Tao Han, Songhong Wei and Wenjing Zheng

Agriculture 2025, 15(12), 1319; https://doi.org/10.3390/agriculture15121319 - 19 Jun 2025

Viewed by 313

Abstract

Rice bakanae disease (RBD), a major threat in rice-cropping nations, can reduce rice yield and quality. As it is a seed-borne disease, effective seed detection is crucial. Loop-mediated isothermal amplification (LAMP) can rapidly and specifically amplify DNA at a constant temperature with high [...] Read more.

Rice bakanae disease (RBD), a major threat in rice-cropping nations, can reduce rice yield and quality. As it is a seed-borne disease, effective seed detection is crucial. Loop-mediated isothermal amplification (LAMP) can rapidly and specifically amplify DNA at a constant temperature with high sensitivity. This research uses LAMP to develop rapid RBD pathogen detection systems. Primers were designed targeting the NRPS31 gene of Fusarium fujikuroi and conserved TEF1α sequences of Fusarium asiaticum, Fusarium proliferatum, and Fusarium andiyazi. These reactions at 60 °C for 60 min had a detection limit of 100 pg·μL⁻¹, and LAMP proved applicable in field trials. Full article

(This article belongs to the Special Issue Detection, Diagnostics and Management Control Strategies of Plant Pathogens)

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22 pages, 9695 KB

Open AccessArticle

DAENet: A Deep Attention-Enhanced Network for Cropland Extraction in Complex Terrain from High-Resolution Satellite Imagery

by Yushen Wang, Mingchao Yang, Tianxiang Zhang, Shasha Hu and Qingwei Zhuang

Agriculture 2025, 15(12), 1318; https://doi.org/10.3390/agriculture15121318 - 19 Jun 2025

Viewed by 465

Abstract

Prompt and precise cropland mapping is indispensable for safeguarding food security, enhancing land resource utilization, and advancing sustainable agricultural practices. Conventional approaches faced difficulties in complex terrain marked by fragmented plots, pronounced elevation differences, and non-uniform field borders. To address these challenges, we [...] Read more.

Prompt and precise cropland mapping is indispensable for safeguarding food security, enhancing land resource utilization, and advancing sustainable agricultural practices. Conventional approaches faced difficulties in complex terrain marked by fragmented plots, pronounced elevation differences, and non-uniform field borders. To address these challenges, we propose DAENet, a novel deep learning framework designed for accurate cropland extraction from high-resolution GaoFen-1 (GF-1) satellite imagery. DAENet employs a novel Geometric-Optimized and Boundary-Restrained (GOBR) Block, which combines channel attention, multi-scale spatial attention, and boundary supervision mechanisms to effectively mitigate challenges arising from disjointed cropland parcels, topography-cast shadows, and indistinct edges. We conducted comparative experiments using 8 mainstream semantic segmentation models. The results demonstrate that DAENet achieves superior performance, with an Intersection over Union (IoU) of 0.9636, representing a 4% improvement over the best-performing baseline, and an F1-score of 0.9811, marking a 2% increase. Ablation analysis further validated the indispensable contribution of GOBR modules in improving segmentation precision. Using our approach, we successfully extracted 25,556.98 hectares of cropland within the study area, encompassing a total of 67,850 individual blocks. Additionally, the proposed method exhibits robust generalization across varying spatial resolutions, underscoring its effectiveness as a high-accuracy solution for agricultural monitoring and sustainable land management in complex terrain. Full article

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

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23 pages, 6989 KB

Open AccessArticle

Design of the Vibrating Sieving Mechanism for a Quinoa Combine Harvester and Coupled Analysis of DEM-MBD

by Ruijie Shi, Xiaojing Ren, Fei Dai, Wuyun Zhao and Tianfu Wang

Agriculture 2025, 15(12), 1317; https://doi.org/10.3390/agriculture15121317 - 19 Jun 2025

Viewed by 526

Abstract

Quinoa is renowned for its high nutritional value, which not only meets the nutritional needs of the human body but also makes it a suitable option for individuals with diabetes and celiac disease due to its low sugar and gluten-free characteristics. In China, [...] Read more.

Quinoa is renowned for its high nutritional value, which not only meets the nutritional needs of the human body but also makes it a suitable option for individuals with diabetes and celiac disease due to its low sugar and gluten-free characteristics. In China, the primary cultivation regions of quinoa are the Tibetan Plateau, the Yunnan–Guizhou Plateau, and Northwest China, which are predominantly characterized by hilly and mountainous terrain, resulting in the gradual development of mechanized harvesting processes. The efficacy of the mechanized harvesting process in these regions is suboptimal, exhibiting poor clearance and efficiency. In this paper, the design and MBD-EDEM coupling analysis of the quinoa combine harvester’s cleaning and screening mechanism is carried out to simulate the cleaning process of quinoa threshing materials. The results show that the vibrating screen can complete the forward sliding and dispersed throwing up of the materials and effectively avoid the accumulation of the threshing materials. The coupling results of the permeability of each material in the cleaning and screening mechanism, as well as the vibrating screen movement condition, indicate that when the herringbone screen opening degree is set in the range of 15° to 30°, the cleaning and screening device can achieve a high cleaning efficiency while maintaining a low impurity rate. Field trial data further confirm that within this opening range, the cleaning effect and efficiency both exhibit significant advantages. Full article

(This article belongs to the Section Agricultural Technology)

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26 pages, 389 KB

Open AccessArticle

From Greenwashing to Sustainability: The Mediating Effect of Green Innovation in the Agribusiness Sector on Financial Performance

by Zhongping Wang and Xiaoying Tian

Agriculture 2025, 15(12), 1316; https://doi.org/10.3390/agriculture15121316 - 19 Jun 2025

Cited by 1 | Viewed by 762

Abstract

This study analyses the impact of agricultural greenwashing on financial performance via green innovation. To this end, it employs data from Chinese A-share agribusinesses from 2012 to 2022. The study indicates the following results: (1) the practice of greenwashing (ESG disclosure–performance gap, GW) [...] Read more.

This study analyses the impact of agricultural greenwashing on financial performance via green innovation. To this end, it employs data from Chinese A-share agribusinesses from 2012 to 2022. The study indicates the following results: (1) the practice of greenwashing (ESG disclosure–performance gap, GW) has a significant negative impact on ROA, particularly in non-state firms; (2) green innovation (patents, GI) partially mediates this relationship, with a percentage of 9.09%, as GW diverts research and development resources toward image management. Robustness checks are employed to confirm the results obtained using ROE and lagged models. Property rights moderate the effects: non-state firms are more adversely affected by innovation dependency, while state firms are protected by policies. The “double-edged” mechanism elucidates GW’s short-term legitimacy gains in contrast to long-term innovation suppression and financial decline. The report calls for the establishment of standardised ESG metrics (for example, the disclosure of pesticide residue) and targeted green incentives (for example, SME R&D subsidies) to be aligned with UN SDGs 9.4 (green tech) and 12.6 (responsible production). The present study offers insights into the governance of environmental, social, and governance (ESG) matters within the context of agriculture in China. Full article

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

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25 pages, 5305 KB

Open AccessArticle

Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model

by Zeqing Yang, Jiahui Zhang, Zhimeng Li, Ning Hu and Zhengpan Qi

Agriculture 2025, 15(12), 1315; https://doi.org/10.3390/agriculture15121315 - 19 Jun 2025

Viewed by 426

Abstract

Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. To address these challenges, this study investigates a non-destructive [...] Read more.

Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. To address these challenges, this study investigates a non-destructive approach integrating X-ray imaging and multi-criteria decision (MCD) theory for non-destructive internal defect detection in pears. Internal defects were identified by analyzing grayscale variations in X-ray images. The proposed method combines manual feature-based classifiers, including Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), with a deep convolutional neural network (DCNN) model within an MCD-based fusion framework. Experimental results demonstrated that the fused model achieved a detection accuracy of 97.1%, significantly outperforming individual classifiers. This approach effectively reduced misclassification caused by structural similarities in X-ray images. The study confirms the efficacy of X-ray imaging coupled with multi-classifier fusion for accurate and non-destructive internal quality evaluation of pears, offering practical value for fruit grading and post-harvest management in the pear industry. Full article

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

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15 pages, 245 KB

Open AccessArticle

Remuneration for Own Labour in Family-Run Dairy Farms Versus the Salaries and Wages in Non-Agricultural Sectors of the Economy—Evaluation of the Situation in Poland in 2005–2022

by Andrzej Parzonko, Tomasz Wojewodzic, Marta Czekaj, Renata Płonka and Anna Justyna Parzonko

Agriculture 2025, 15(12), 1314; https://doi.org/10.3390/agriculture15121314 - 19 Jun 2025

Viewed by 545

Abstract

Income level is a key indicator of the standard of living and the economic efficiency of undertaken activities. This paper aims to evaluate the earnings of Polish dairy farmers compared to those in other economic sectors between 2005 and 2022. The analysis covered [...] Read more.

Income level is a key indicator of the standard of living and the economic efficiency of undertaken activities. This paper aims to evaluate the earnings of Polish dairy farmers compared to those in other economic sectors between 2005 and 2022. The analysis covered 1688 family-run farms that participated continuously in the FADN system throughout the study period, with particular emphasis on farms that expanded their dairy cow herds. The remuneration for the labour of farmers and their families was estimated ex post by subtracting the opportunity costs of owned land and capital from farm income. The alternative cost of engaging one’s own land was determined on the basis of actual rental prices for farmland occurring in the surveyed farm groups in the years analysed. This information is collected in the FADN system from which the studied group of farms was drawn. The basis for determining the alternative cost of involvement of own capital was the average interest rates on deposits for households, concluded for a period of 6 months to 1 year inclusive, reported by the National Bank of Poland. The analysed population was divided into seven groups based on the number of dairy cows maintained. The analysis focused on two three-year reference periods: 2005–2007 and 2020–2022. The results were compared with average salaries and wages in non-agricultural sectors of the economy. Structural changes in agriculture, increased productivity, and the expansion of production scale in dairy farms indicate a growing professionalisation of the sector. The rise in farm incomes during the analysed period contributed to a significant increase in the remuneration for farmers’ and their families’ labour. The highest growth in remuneration was observed among farms with the greatest production potential and scale. While in 2005–2007 the remuneration for labour in dairy farms was lower than in non-agricultural sectors, this situation changed in 2020–2022. During this latter period, the average remuneration for labour on dairy farms slightly exceeded the average salary and wages in other sectors of the economy. Full article

(This article belongs to the Special Issue Economics of Milk Production and Processing)

24 pages, 7335 KB

Open AccessArticle

Soil Organic Matter Content Prediction Using Multi-Input Convolutional Neural Network Based on Multi-Source Information Fusion

by Li Guo, Qin Gao, Mengyi Zhang, Panting Cheng, Peng He, Lujun Li, Dong Ding, Changcheng Liu, Francis Collins Muga, Masroor Kamal and Jiangtao Qi

Agriculture 2025, 15(12), 1313; https://doi.org/10.3390/agriculture15121313 - 19 Jun 2025

Viewed by 550

Abstract

Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destructive analysis. While significant progress has been [...] Read more.

Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destructive analysis. While significant progress has been made in spectral inversion for SOM prediction, its accuracy still lags behind traditional chemical methods. This study proposes a novel approach to predict SOM content by integrating spectral, texture, and color features using a three-branch convolutional neural network (3B-CNN). Spectral reflectance data (400–1000 nm) were collected using a portable hyperspectral imaging device. The top 15 spectral bands with the highest correlation were selected from 260 spectral bands using the Correlation Coefficient Method (CCM), Boruta algorithm, and Successive Projections Algorithm (SPA). Compared to other methods, CCM demonstrated superior dimensionality reduction performance, retaining bands highly correlated with SOM, which laid a solid foundation for multi-source data fusion. Additionally, six soil texture features were extracted from soil images taken with a smartphone using the gray-level co-occurrence matrix (GLCM), and twelve color features were obtained through the color histogram. These multi-source features were fused via trilinear pooling. The results showed that the 3B-CNN model, integrating multi-source data, performed exceptionally well in SOM prediction, with an R² of 0.87 and an RMSE of 1.68, a 23% improvement in R² compared to the 1D-CNN model using only spectral data. Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R² improvements ranging from 4% to 11%. This study demonstrates the potential of multi-source data fusion in accurately predicting SOM content, enabling rapid assessment at the field scale and providing a scientific basis for precision fertilization and agricultural management. Full article

(This article belongs to the Section Agricultural Soils)

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16 pages, 1037 KB

Open AccessArticle

Assessing Nutrient Losses and Recycling in Sweet Cherry Orchards: A Yield-Based Approach

by Ilias Karampatzakis, Fotis Bilias, Chrysanthi Polychroniadou, Georgia Tanou, Panagiotis Kekelis, Aphrodite Theofilidou, Georgios Giannopoulos, Athina Pavlatou-Ve and Vassilis Aschonitis

Agriculture 2025, 15(12), 1312; https://doi.org/10.3390/agriculture15121312 - 18 Jun 2025

Viewed by 570

Abstract

Mineral nutrition management in sweet cherry orchards remains a critical challenge due to the lack of site-specific fertilization guidelines, particularly in Greece, a significant cherry-producing country. This study aimed to develop a predictive framework for total nutrient losses in sweet cherry orchards by [...] Read more.

Mineral nutrition management in sweet cherry orchards remains a critical challenge due to the lack of site-specific fertilization guidelines, particularly in Greece, a significant cherry-producing country. This study aimed to develop a predictive framework for total nutrient losses in sweet cherry orchards by proposing simplified estimations using fresh fruit yield as the sole input variable. Field experiments were conducted in two orchards with distinct rootstocks (MxM 14 and CAB-6P), analyzing soil properties, leaf nutrient status, and uptake patterns on different plant components. Results indicated that despite differences in soil texture and pH, nutrient availability was generally sufficient, with only Fe and Zn marginally below optimal levels in leaf tissue. Principal Component Analysis (PCA) revealed distinct nutrient distribution patterns, with N evenly distributed across fruits, peduncles, and prunings, while K was concentrated in fruits and peduncles, and Ca and Mg predominantly in fallen leaves. Notably, K was redistributed from leaves to fruits under high yields, evidenced by negative correlations between leaf biomass and K uptake. Strong relationships (r² > 0.8) were found between fresh fruit yield and uptake of N, P, K, Mg, B, and Cu, enabling reliable predictions of total nutrient losses. Estimated annual nutrient removals were 85.6 kg ha⁻¹ N, 8.94 kg ha⁻¹ P, 42.7 kg ha⁻¹ K, and 12.0 kg ha⁻¹ Mg, with significant fractions retained in prunings and fallen leaves (e.g., 51.8 kg ha⁻¹ N, 6.2 kg ha⁻¹ P). The developed yield-based models provide a practical tool for optimizing fertilization strategies, while our findings highlight the potential for nutrient recycling through sustainable residue management. Full article

(This article belongs to the Special Issue Advanced Cultivation Technologies for Horticultural Crops Production)

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16 pages, 302 KB

Open AccessArticle

Poultry Eco-Controls: Performance and Accounting

by Valquíria D. V. Rodrigues, Alcido E. Wander and Fabricia S. da Rosa

Agriculture 2025, 15(12), 1311; https://doi.org/10.3390/agriculture15121311 - 18 Jun 2025

Viewed by 469

Abstract

This study aims to evaluate environmental performance indicators and eco-controls in the poultry production chain in Goiás, with a focus on forest management, waste generation, water resources, energy use, emissions, and environmental accounting. A mixed-methods approach was used, combining qualitative and quantitative data [...] Read more.

This study aims to evaluate environmental performance indicators and eco-controls in the poultry production chain in Goiás, with a focus on forest management, waste generation, water resources, energy use, emissions, and environmental accounting. A mixed-methods approach was used, combining qualitative and quantitative data from 13 agro-industrial companies, 230 farms, and 816 broiler houses. The results highlight the role of environmental management accounting (EMA) in monitoring and improving environmental practices, supporting continuous performance assessment. Econometric analysis revealed a positive link between sustainability practices and economic growth, as measured by GDP per capita. However, productive capacity and energy efficiency showed no significant impact at the 5% level. The study limitations include the focus on a single region and industry, which may limit the generalizability of the findings. Future research should expand to other chains and regions to assess broader applicability and explore the public policy impacts on environmental sustainability, as well as the impact of public policies on environmental sustainability within the sector. Full article

(This article belongs to the Special Issue Enhancing Water Use Efficiency in Poultry Production)

28 pages, 5867 KB

Open AccessArticle

Tomato Ripening Detection in Complex Environments Based on Improved BiAttFPN Fusion and YOLOv11-SLBA Modeling

by Yan Hao, Lei Rao, Xueliang Fu, Hao Zhou and Honghui Li

Agriculture 2025, 15(12), 1310; https://doi.org/10.3390/agriculture15121310 - 18 Jun 2025

Viewed by 574

Abstract

Several pressing issues have been revealed by deep learning-based tomato ripening detection technology in intricate environmental applications: The ripening transition stage distinction is not accurate enough, small target tomato detection is likely to miss, and the detection technology is more susceptible to variations [...] Read more.

Several pressing issues have been revealed by deep learning-based tomato ripening detection technology in intricate environmental applications: The ripening transition stage distinction is not accurate enough, small target tomato detection is likely to miss, and the detection technology is more susceptible to variations in light. Based on the YOLOv11 model, a YOLOv11-SLBA tomato ripeness detection model was presented in this study. First, SPPF-LSKA is used in place of SPPF in the backbone section, greatly improving the model’s feature discrimination performance in challenging scenarios including dense occlusion and uneven illumination. Second, a new BiAttFPN hierarchical progressive fusion is added in the neck area to increase the feature retention of small targets during occlusion. Lastly, the feature separability of comparable categories is significantly enhanced by the addition of the auxiliary detection head DetectAux. In this study, comparative experiments are carried out to confirm the model performance. Under identical settings, the YOLOv11-SLBA model is compared to other target detection networks, including Faster R-CNN, SSD, RT-DETR, YOLOv7, YOLOv8, and YOLOv11. With 2.7 million parameters and 10.9 MB of model memory, the YOLOv11-SLBA model achieves 92% P, 83.5% R, 91.3% mAP50, 64.6% mAP50-95, and 87.5% F1-score. This is a 3.4% improvement in accuracy, a 1.5% improvement in average precision, and a 1.6% improvement in F1-score when compared to the baseline model YOLOv11. It outperformed the other comparison models in every indication and saw a 1.6% improvement in score. Furthermore, the tomato-ripeness1public dataset was used to test the YOLOv11-SLBA model, yielding model p values of 78.6%, R values of 91.5%, mAP50 values of 93.7%, and F1-scores of 84.6%. This demonstrates that the model can perform well across a variety of datasets, greatly enhances the detection generalization capability in intricate settings, and serves as a guide for the algorithm design of the picking robot vision system. Full article

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

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18 pages, 3051 KB

Open AccessArticle

Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing

by Shanxin Zhang, Jibo Yue, Xiaoyan Wang, Haikuan Feng, Yang Liu and Meiyan Shu

Agriculture 2025, 15(12), 1309; https://doi.org/10.3390/agriculture15121309 - 18 Jun 2025

Viewed by 624

Abstract

The accurate estimation of corn canopy structure and light conditions is essential for effective crop management and informed variety selection. This study introduces CCSNet, a deep learning-based semantic segmentation model specifically developed to extract fractional coverages of soil, illuminated vegetation, and shaded vegetation [...] Read more.

The accurate estimation of corn canopy structure and light conditions is essential for effective crop management and informed variety selection. This study introduces CCSNet, a deep learning-based semantic segmentation model specifically developed to extract fractional coverages of soil, illuminated vegetation, and shaded vegetation from high-resolution corn canopy images acquired by UAVs. CCSNet improves segmentation accuracy by employing multi-level feature fusion and pyramid pooling to effectively capture multi-scale contextual information. The model was evaluated using Pixel Accuracy (PA), mean Intersection over Union (mIoU), and Recall, and was benchmarked against U-Net, PSPNet and UNetFormer. On the test set, CCSNet utilizing a ResNet50 backbone achieved the highest accuracy, with an mIoU of 86.42% and a PA of 93.58%. In addition, its estimation of fractional coverage for key canopy components yielded a root mean squared error (RMSE) ranging from 3.16% to 5.02%. Compared to lightweight backbones (e.g., MobileNetV2), CCSNet exhibited superior generalization performance when integrated with deeper backbones. These results highlight CCSNet’s capability to deliver high-precision segmentation and reliable phenotypic measurements. This provides valuable insights for breeders to evaluate light-use efficiency and facilitates intelligent decision-making in precision agriculture. Full article

(This article belongs to the Special Issue Research Advances in Perception for Agricultural Robots)

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24 pages, 9205 KB

Open AccessArticle

Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images

by Juxia Wang, Yu Zhang, Fei Han, Zhenpeng Shi, Fu Zhao, Fengzi Zhang, Weizheng Pan, Zhiyong Zhang and Qingliang Cui

Agriculture 2025, 15(12), 1308; https://doi.org/10.3390/agriculture15121308 - 18 Jun 2025

Cited by 2 | Viewed by 551

Abstract

The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status [...] Read more.

The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status of apple trees. In this study, the canopy leaves of apple trees at different growth stages in the same year were taken as the research object, and remote sensing images of fruit trees in different growth stages (flower-falling stage, fruit-setting stage, fruit expansion stage, fruit-coloring stage and fruit-maturing stage) were acquired via a DJI MAVIC 3 multispectral unmanned aerial vehicle (UAV). Then, the spectral reflectance was extracted to calculate 15 common vegetation indexes as eigenvalues, the 5 vegetation indexes with the highest correlation were screened out through Pearson correlation analysis as the feature combination, and the measured SPAD values in the leaves of the fruit trees were gained using a handheld chlorophyll meter in the same stages. The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). Additionally, the model performance was assessed by selecting the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. Among the estimation models in the different growth stages, the model accuracy for the fruit expansion stage is the highest, with R² = 0.787, RMSE = 0.87 and MAE = 0.644. The RF model, which outperforms the other models in terms of the prediction effect in multiple growth stages, can effectively predict the SPAD value in the leaves of apple trees and provide a reference for the growth status monitoring and precise management of orchards. Full article

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

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18 pages, 1088 KB

Open AccessArticle

Introducing Legumes into Wheat–Maize Rotation Complicates Soil Microbial Co-Occurrence Network and Reduces Soil Allelochemicals in Succeeding Wheat Season

by Yaqian Yan, Haiyang Jin, Fei Zheng, Xiwen Yang, Hang Song, Jiarui Wang, Baoting Fang, Hongjian Cheng, Xiangdong Li and Dexian He

Agriculture 2025, 15(12), 1307; https://doi.org/10.3390/agriculture15121307 - 18 Jun 2025

Cited by 1 | Viewed by 641

Abstract

Increasing species richness through rotation is considered a promising measure to enhance agroecosystem functions and services. However, the legacy effects of introducing legumes into a wheat–maize rotation in the North China Plain on soil microecology, especially the soil metabolome, in the succeeding wheat [...] Read more.

Increasing species richness through rotation is considered a promising measure to enhance agroecosystem functions and services. However, the legacy effects of introducing legumes into a wheat–maize rotation in the North China Plain on soil microecology, especially the soil metabolome, in the succeeding wheat season have not been elucidated. This study established three cropping systems: (1) a continuous winter wheat–summer maize rotation (M), (2) a winter wheat–summer peanut (summer maize) rotation (PM), and (3) a winter wheat–summer soybean (summer maize) rotation (SM). The soil physicochemical properties, microbial communities, and metabolomes were analyzed at the stage of the succeeding wheat crop. Introducing peanuts or soybeans into a wheat–maize rotation significantly reduced the soil bacterial abundance and increased the soil fungal Shannon index. This rotation adjustment had a substantial impact on the structure and taxa composition of the soil microbial community. Crop diversification increased the number of total edges, the average degree, and the average number of neighbors in the soil microbial co-occurrence network. Different crop rotations significantly affected the soil metabolic profiles in the positive and negative ion modes. Crop diversification significantly reduced the abundance of coumarin and coumaric acid in the soils. In conclusion, introducing peanuts or soybeans into a wheat–maize rotation could increase the soil fungal community diversity, change the soil microbial community structure and taxa composition, increase the complexity of the soil microbial ecological network, and reduce the abundance of soil allelochemicals. Our study demonstrated the continuity of the impact of crop rotation on soil ecology, and revealed the ecological advantages of crop diversification from the perspective of soil microbiology and metabolomics. Full article

(This article belongs to the Section Agricultural Soils)

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17 pages, 3149 KB

Open AccessArticle

Dietary Digestible Protein Requirement in Finishing Pigs: A Study for Experimental Determination and Verification

by Shengkai Li, Hui Ma, Jianliang Wu, Jihe Lu, Shiyan Qiao, Xiangfang Zeng and Junyan Zhou

Agriculture 2025, 15(12), 1306; https://doi.org/10.3390/agriculture15121306 - 17 Jun 2025

Viewed by 539

Abstract

Crude protein, as a traditional standard for characterizing dietary nitrogen content, fails to reflect protein bioavailability. Digestible protein (DP) emphasizes the importance of total available proteins and offers better adaptability in low-protein diversified diets. The objective of this study was to establish and [...] Read more.

Crude protein, as a traditional standard for characterizing dietary nitrogen content, fails to reflect protein bioavailability. Digestible protein (DP) emphasizes the importance of total available proteins and offers better adaptability in low-protein diversified diets. The objective of this study was to establish and validate the digestible protein (DP) requirement for 80–110 kg finishing pigs (Duroc × Yorkshire × Landrace). In Experiment 1, 450 pigs were fed diets with graded DP levels (8.82–11.26%). Linear and quadratic regression models identified 9.55% DP as the optimal level, optimizing average daily gain and feed efficiency (R² ≥ 0.94). Experiment 2 validated this requirement using three diet treatments and 270 pigs: high-protein traditional, low-protein traditional, and low-protein diversified. No significant differences were observed in growth performance, carcass traits, or meat quality among diets, confirming the robustness of 9.55% DP across formulations. Plasma urea nitrogen and total amino acids increased linearly with DP (p < 0.05), while hepatic transcriptomics revealed immune and metabolic partial impairments in high-protein traditional diet pigs, which may be linked to nitrogen overload. Muscle tissues from different treatment groups showed minimal transcriptional differences, emphasizing efficient protein utilization when amino acid requirements are met. This study demonstrates that 9.55% DP, combined with balanced amino acids, supports productivity in both traditional and diversified diets, reducing reliance on resource-intensive feed ingredients. These findings advocate for DP as a precise metric in swine production, thereby promoting sustainable development. Full article

(This article belongs to the Section Farm Animal Production)

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23 pages, 5834 KB

Open AccessArticle

Comparative Analysis of the Expression of Genes Involved in Fatty Acid Synthesis Across Camelina Varieties

by Elisa Gómez, Gregorio Hueros, David Mostaza-Colado, Aníbal Capuano, Mercedes Uscola and Pedro V. Mauri

Agriculture 2025, 15(12), 1305; https://doi.org/10.3390/agriculture15121305 - 17 Jun 2025

Viewed by 521

Abstract

Camelina sativa (L.) Crantz, a native European oilseed crop of the Brassicaceae family, is notable for its short life cycle, making it well-suited for crop rotation and diversification. Its seeds contain a high content of oil (36–47%) that is rich in polyunsaturated fatty [...] Read more.

Camelina sativa (L.) Crantz, a native European oilseed crop of the Brassicaceae family, is notable for its short life cycle, making it well-suited for crop rotation and diversification. Its seeds contain a high content of oil (36–47%) that is rich in polyunsaturated fatty acids (PUFAs) such as alpha-linolenic acid (ALA, C18:3, Ω-3) and linoleic acid (LA, C18:2, Ω-6). This oil has diverse industrial applications, including low-emission biofuels, animal feed, pharmaceuticals, biolubricants, bioplastics, and cosmetics. We analyzed the expression of seven key enzymes involved in fatty acid biosynthesis across nine C. sativa accessions at three stages of silique development using highly efficient qRT-PCR assays designed for the target genes and a normalizing control. Our detailed expression analysis revealed significant variation across varieties, with only the gene FAB2c exhibiting genotype-independent expression, indicating a constitutive and essential role in monounsaturated fatty acid (MUFA) biosynthesis. Other genes showed significant interactions between the variety and developmental stage, highlighting the combined influences of genetic background and silique maturation on gene regulation. V18 emerges as particularly promising, exhibiting elevated expression of genes linked to PUFA and VLCFA biosynthesis—traits of significance for food, biofuel, and industrial applications. These findings, together with the developed qRT-PCR assays, provide valuable tools for selecting Camelina varieties with optimized genetic profiles, highlighting the potential of harnessing natural transcriptional diversity for crop improvement. Full article

(This article belongs to the Special Issue Crop Yield Improvement in Genetic and Biology Breeding)

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27 pages, 1323 KB

Open AccessReview

Factors Influencing the Formation, Development of Buds, and Flowering of Temperate Fruit Trees

by Iwona Szot and Grzegorz P. Łysiak

Agriculture 2025, 15(12), 1304; https://doi.org/10.3390/agriculture15121304 - 17 Jun 2025

Cited by 1 | Viewed by 1334

Abstract

The condition for the formation of fruit on fruit plants is the presence of flower buds, flowering and proper pollination/fertilisation of flowers. Fruit trees and shrubs are perennial plants, and the processes of flower bud formation and flowering are distant in time. The [...] Read more.

The condition for the formation of fruit on fruit plants is the presence of flower buds, flowering and proper pollination/fertilisation of flowers. Fruit trees and shrubs are perennial plants, and the processes of flower bud formation and flowering are distant in time. The formation of flower buds occurs in the year preceding flowering and fruiting. The number and quality of flowers are the basic factors that determine the potential yield of fruit trees. Therefore, the review focuses on a thorough review of the latest research on the various stages in the development of trees, in which the processes that determine their flowering take place. The greatest emphasis was placed on the influence of factors that determine the yield of trees after the juvenile stage. Climate change leading to global warming will undoubtedly affect the formation of flower buds, which determine the size of crops. To avoid the unforeseen effects of abiotic factors on the availability of raw materials, such as fruits, it is good to diversify the structure of cultivated plants. Most fruit plants come from the Rosaceae family, so they have many pathogens and pests in common. To increase crop, economic, and habitat biodiversity, it is necessary to look for other, more genetically distant, sometimes even less known fruit-bearing species. Full article

(This article belongs to the Section Crop Production)

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14 pages, 3406 KB

Open AccessArticle

Impacts of Fertilization Regimes on Yield and Grain Quality in Diverse Rice Genotypes Under Cold Temperate Conditions

by Guo-Hua Ding, Bao-Jia Han, Kai Liu, Jin-Song Zhou, Liang-Zi Cao, Lei Lei, Liang-Ming Bai, Guang Yang, Yu Luo, Man-Li Wang, Wen-Xiu Hou, Yan-Song Ma, Yang Ren, Yan-Jiang Feng and Shi-Chen Sun

Agriculture 2025, 15(12), 1303; https://doi.org/10.3390/agriculture15121303 - 17 Jun 2025

Viewed by 517

Abstract

The purpose of this research is to clarify the genetic groups associated with high-quality germplasms with different genotypes and investigate the responses of their yield and quality traits to varied fertilization regimes, thereby providing references for premium breeding and cultivation practices. A total [...] Read more.

The purpose of this research is to clarify the genetic groups associated with high-quality germplasms with different genotypes and investigate the responses of their yield and quality traits to varied fertilization regimes, thereby providing references for premium breeding and cultivation practices. A total of 29 samples, including 15 newly developed lines and 14 elite cultivars, were analyzed as to their genetic structure. A split-plot field experiment was conducted to evaluate the responses of yield, quality, and agronomic traits to different fertilization treatments. The 29 samples were classified into three genetic groups. Line 1, developed by our team, formed a unique group, while Lines 3 and 8 constituted another distinct group. In the 2022 trial, the protein content ranged from 6.8% to 9.0%, amylose content from 17.9% to 20.1%, and taste value from 73.8 to 85.7. The top five samples in yield were Line 4, Line 2, Line 5, Line 6, and Line 1, with significant differences among them (p < 0.05) and markedly higher yields compared to other samples. For taste value, Longdao 17029, Line 2, Line 4, Line 5, and Line 6 ranked highest, with Longdao 17029 exhibiting significantly superior taste scores (p < 0.05). Fertilizer treatments ranked by yield were as follows: Treatment 1 (N₁₂₀P₁₀₀K₁₀₀) > Treatment 4 (Longfutianxia slow-release fertilizer) > Treatment 2 (N₉₆P₁₀₀K₁₀₀ + bio-organic fertilizer) > Treatment 3 (N₀P₀K₀), with significant differences (p < 0.05). Treatment 2 achieved a significantly higher taste value than the others (p < 0.05). In the 2023 trial, Longdao 17029 demonstrated higher leaf color index and net photosynthetic rate during the grain-filling stage, compared to Longdao 18 and Longdao 21. Lines 1, 3, and 8 are valuable for broadening the genetic diversity of rice in cold regions. Line 4 and Longdao 17029 show potential as novel germplasms for improving yield and taste quality. Moderate nitrogen reduction, combined with bio-organic fertilizer application, enhances rice taste value. Full article

(This article belongs to the Section Crop Production)

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33 pages, 7310 KB

Open AccessArticle

Integrating Geodetector and GTWR to Unveil Spatiotemporal Heterogeneity in China’s Agricultural Carbon Emissions Under the Dual Carbon Goals

by Huae Dang, Yuanjie Deng, Yifeng Hai, Hang Chen, Wenjing Wang, Miao Zhang, Xingyang Liu, Can Yang, Minghong Peng, Dingdi Jize, Mei Zhang and Long He

Agriculture 2025, 15(12), 1302; https://doi.org/10.3390/agriculture15121302 - 17 Jun 2025

Cited by 1 | Viewed by 663

Abstract

Against the backdrop of intensifying global climate change and deepening sustainable development goals, the low-carbon transformation of agriculture, as a major greenhouse gas emission source, holds significant strategic importance for achieving China’s “carbon peaking and carbon neutrality” (referred to as the “dual carbon”) [...] Read more.

Against the backdrop of intensifying global climate change and deepening sustainable development goals, the low-carbon transformation of agriculture, as a major greenhouse gas emission source, holds significant strategic importance for achieving China’s “carbon peaking and carbon neutrality” (referred to as the “dual carbon”) targets. To reveal the spatiotemporal evolution characteristics and complex driving mechanisms of agricultural carbon emissions (ACEs), this study constructs a comprehensive accounting framework for agricultural carbon emissions based on provincial panel data from China spanning 2000 to 2023. The framework encompasses three major carbon sources—cropland use, rice cultivation, and livestock farming—enabling precise quantification of total agricultural carbon emissions. Furthermore, spatial-temporal distribution patterns are characterized using methodologies including standard deviational ellipse (SDE) and spatial autocorrelation analysis. For driving mechanism identification, the Geodetector and Geographically and Temporally Weighted Regression (GTWR) models are employed. The former quantifies the spatial explanatory power and interaction effects of driving factors, while the latter enables dynamic estimation of factor influence intensities across temporal and spatial dimensions, jointly revealing significant spatiotemporal heterogeneity in driving mechanisms. Key findings: (1) temporally, total ACEs exhibit fluctuating growth, while emission intensity has significantly decreased, indicating the combined effects of policy regulation and technological advancements; (2) spatially, emissions display an “east-high, west-low” pattern, with an increasing number of hotspot areas and a continuous shift of the emission centroid toward the northwest; and (3) mechanistically, agricultural gross output value is the primary driving factor, with its influence fluctuating in response to economic and policy changes. The interactions among multiple factors evolve over time, transitioning from economy-driven to synergistic effects of technology and climate. The GTWR model further reveals the spatial and temporal variations in the impacts of each factor. This study recommends formulating differentiated low-carbon agricultural policies based on regional characteristics, optimizing industrial structures, enhancing modernization levels, strengthening regional collaborative governance, and promoting the synergistic development of climate and agriculture. These measures provide a scientific basis and policy reference for achieving the “dual carbon” goals. Full article

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

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11 pages, 5164 KB

Open AccessArticle

Molecular Characterization and Ex Situ Conservation of Wild Grapevines Grown in the Area Around the Neolithic Settlement of Dikili Tash, Greece

by Georgios Merkouropoulos, Ioannis Ganopoulos, Georgios Doupis, Erika Maul and Franco Röckel

Agriculture 2025, 15(12), 1301; https://doi.org/10.3390/agriculture15121301 - 17 Jun 2025

Viewed by 474

Abstract

Dikili Tash is a Neolithic settlement that lies next to the ruins of the ancient city of Philippi on the north-eastern part of Greece. A recent archaeological excavation has unearthed charred grapevine pips and pressings together with two-handed clay cups, jugs, and jars [...] Read more.

Dikili Tash is a Neolithic settlement that lies next to the ruins of the ancient city of Philippi on the north-eastern part of Greece. A recent archaeological excavation has unearthed charred grapevine pips and pressings together with two-handed clay cups, jugs, and jars that date to 4300 BC. The majority of the pips were found to be Vitis vinifera ssp. sylvestris. Natural populations of this species have been localized in the valley surrounding Dikili Tash and also on Mt Pangaion and Mt Lekani, which flank the valley. Fifty-one samples from these modern populations have been analyzed using microsatellites on twenty microsatellite loci, and a dendrogram has been constructed showing the genetic closeness of the samples analyzed. Cuttings from all the vines analyzed are currently rooted and grown in the Hellenic Agricultural Organization—DIMITRA (ELGO-DIMITRA) greenhouse facilities in Lykovryssi (Athens) with the aim to, eventually, be transplanted in the grapevine, thus establishing the first V. sylvestris ex situ conservation site in Greece. Full article

(This article belongs to the Section Crop Genetics, Genomics and Breeding)

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17 pages, 1269 KB

Open AccessArticle

Key Influencing Factors in the Variation in Livestock Carbon Emissions in the Grassland Region of Gannan Prefecture, China (2009–2024)

by Guohua Chang, Jinxiang Wang, Panliang Liu, Qi Wang, Fanxiang Han, Chao Wang, Tawatchai Sumpradit and Tianpeng Gao

Agriculture 2025, 15(12), 1300; https://doi.org/10.3390/agriculture15121300 - 17 Jun 2025

Viewed by 548

Abstract

Research was conducted in Gannan Prefecture, China, to better understand the characteristics of carbon emissions and sequestration in areas dominated by animal husbandry. The emission factor method was used to calculate and analyze changes in carbon emissions from 2009 to 2024. The region’s [...] Read more.

Research was conducted in Gannan Prefecture, China, to better understand the characteristics of carbon emissions and sequestration in areas dominated by animal husbandry. The emission factor method was used to calculate and analyze changes in carbon emissions from 2009 to 2024. The region’s average annual carbon emissions from animal husbandry are 774,286 t C-eq (2,839,049 t CO₂eq), with enteric emissions from cattle being the biggest contributor. However, as the number of locally raised cattle and sheep has decreased, carbon emissions have gradually fallen at an average annual rate of −1.0%. The annual average total carbon sequestration of vegetation in the region is 6,861,535 t C-eq, and the carbon content in underground biomass is higher than that in aboveground biomass, making it the main contributor to grassland carbon sequestration. Carbon sequestration from grassland vegetation is greater than the carbon emissions from animal husbandry, which means that the entire production system is currently a carbon sink. Meanwhile, the analysis of land-use carbon sequestration found that the annual average total sequestration by forests and grasslands over the same time period was 752,327 t C-eq, and sequestration is increasing at an annual rate of 1.4%, primarily driven by the progressive expansion of forested areas. Although the regional carbon emissions from animal husbandry are lower than the carbon sequestration, developing a science-based animal husbandry plan aligned with regional ecological thresholds, continuing to implement grass–livestock balance management measures, and preventing livestock numbers from exceeding their ecological carrying capacity remain critical to promoting sustainable coordination between livestock economies and ecological conservation. Full article

(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

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23 pages, 5318 KB

Open AccessArticle

Noise Testing of the Conveyor Trough Sprocket and Surface Noise Reduction Performance Evaluation of the Cavity Structure in a Combine Harvester

by Jianpeng Jing, Hongyan Sun, Runzhi Liang, Shuren Chen, Zhong Tang, Xiaoying He and Yuxuan Chen

Agriculture 2025, 15(12), 1299; https://doi.org/10.3390/agriculture15121299 - 17 Jun 2025

Cited by 2 | Viewed by 612

Abstract

This study investigates noise detection and damping-based noise mitigation strategies for cavity structures, with a specific focus on addressing noise issues in the conveyor trough of combine harvesters. Despite its practical significance, research on the noise generation mechanisms, transmission paths, and control measures [...] Read more.

This study investigates noise detection and damping-based noise mitigation strategies for cavity structures, with a specific focus on addressing noise issues in the conveyor trough of combine harvesters. Despite its practical significance, research on the noise generation mechanisms, transmission paths, and control measures for conveyor troughs remains limited, particularly under varying operational conditions. To bridge this gap, this work integrates experimental measurements with numerical simulations to systematically analyze and optimize the noise reduction performance of the conveyor trough. Noise measurements were conducted using the sound intensity method, revealing sound pressure levels in the range of 93–95 dB. Frequency spectrum analysis identified key noise sources and dominant frequency components. Finite element analysis (FEA) and vibration modal testing were performed to uncover critical noise-inducing factors, including chain meshing impacts and structural resonances. Based on these findings, a damping optimization strategy was proposed by incorporating constrained damping layers to attenuate vibration and reduce noise in targeted frequency bands. The effectiveness of this approach was validated through multiple coherence analysis, which confirmed significant suppression of structural vibration noise in the 0–500 Hz range, while experimental results showed that the optimized conveyor trough structure achieved a maximum reduction of 0.4071 dB in continuous equivalent A-weighted sound pressure under load conditions. This research provides a comprehensive methodology for noise control and structural optimization of conveyor trough systems, offering valuable theoretical and practical insights for enhancing the operational comfort and environmental performance of combine harvesters. Full article

(This article belongs to the Section Agricultural Technology)

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17 pages, 4171 KB

Open AccessArticle

Comparative Assessment of Injection and Compression Molding on Soy Protein Bioplastic Matrices for Controlled Iron Release in Horticulture

by Daniel Castro-Criado, Mercedes Jiménez-Rosado, Víctor M. Pérez-Puyana and Alberto Romero

Agriculture 2025, 15(12), 1298; https://doi.org/10.3390/agriculture15121298 - 17 Jun 2025

Viewed by 562

Abstract

Conventional horticultural fertilization frequently leads to nutrient loss and environmental contamination, driving interest in biodegradable controlled-release systems. This work developed soy protein isolate (SPI) matrices containing 5 wt.% FeSO₄·7H₂O using injection. The matrices were evaluated for crosslinking, mechanical properties, [...] Read more.

Conventional horticultural fertilization frequently leads to nutrient loss and environmental contamination, driving interest in biodegradable controlled-release systems. This work developed soy protein isolate (SPI) matrices containing 5 wt.% FeSO₄·7H₂O using injection. The matrices were evaluated for crosslinking, mechanical properties, water uptake (WUC), soluble matter loss (SML), iron-release kinetics in water and soil, and biodegradability under composting conditions. Injection-molded samples achieved very high crosslinking with moderate rigidity and water absorption and delivered iron rapidly in water, while compression-molded samples exhibited slightly lower crosslinking but greater stiffness, higher WUC, minimal SML, and sustained iron release. Notably, both processing methods yielded comparable iron-release profiles in soil and complete biodegradation within 71 days. Overall, compression molding produces SPI-based matrices with superior mechanical strength and water retention, positioning them as an ideal solution for long-lasting, sustainable nutrient delivery in horticulture. Full article

(This article belongs to the Section Agricultural Soils)

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28 pages, 3163 KB

Open AccessReview

Review on Key Technologies for Autonomous Navigation in Field Agricultural Machinery

by Hongxuan Wu, Xinzhong Wang, Xuegeng Chen, Yafei Zhang and Yaowen Zhang

Agriculture 2025, 15(12), 1297; https://doi.org/10.3390/agriculture15121297 - 17 Jun 2025

Cited by 1 | Viewed by 1530

Abstract

Autonomous navigation technology plays a crucial role in advancing smart agriculture by enhancing operational efficiency, optimizing resource utilization, and reducing labor dependency. With the rapid integration of information technology, modern agricultural machinery increasingly incorporates advanced techniques such as high-precision positioning, environmental perception, path [...] Read more.

Autonomous navigation technology plays a crucial role in advancing smart agriculture by enhancing operational efficiency, optimizing resource utilization, and reducing labor dependency. With the rapid integration of information technology, modern agricultural machinery increasingly incorporates advanced techniques such as high-precision positioning, environmental perception, path planning, and path-tracking control. This paper presents a comprehensive review of recent advancements in these core technologies, systematically analyzing their methodologies, advantages, and application scenarios. Despite notable progress, considerable challenges persist, primarily due to the unstructured nature of farmland, varying terrain conditions, and the demand for robust and adaptive control strategies. This review also discusses current limitations and outlines prospective research directions, aiming to provide valuable insights for the future development and practical deployment of autonomous navigation systems in agricultural machinery. Future research is expected to focus on enhancing multi-modal perception under occlusion and variable lighting conditions, developing terrain-aware path planning algorithms that adapt to irregular field boundaries and elevation changes and designing robust control strategies that integrate model-based and learning-based approaches to manage disturbances and non-linearity. Furthermore, tighter integration among perception, planning, and control modules will be crucial for improving system-level intelligence and coordination in real-world agricultural environments. Full article

(This article belongs to the Section Agricultural Technology)

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20 pages, 7033 KB

Open AccessArticle

Nano-Copper Supplementation Reduces Fecal Copper Excretion and Enhances Piglet Performance Under Heat Stress

by Xiarui Xiao, Duo Xu, Haixin Zhang, Qian Xing, Daiwen Chen, Xiangbing Mao, Quyuan Wang, Huifen Wang and Hui Yan

Agriculture 2025, 15(12), 1296; https://doi.org/10.3390/agriculture15121296 - 17 Jun 2025

Viewed by 453

Abstract

This study aimed to evaluate the effects of dietary nano-copper supplementation on growth performance, nutrient digestibility, antioxidant status, inflammatory response, and intestinal barrier function in weanling pigs under heat stress conditions. Forty 20-day-old weaned weanling pigs (Yorkshire × Landrace × Duroc) weighing 6.49 [...] Read more.

This study aimed to evaluate the effects of dietary nano-copper supplementation on growth performance, nutrient digestibility, antioxidant status, inflammatory response, and intestinal barrier function in weanling pigs under heat stress conditions. Forty 20-day-old weaned weanling pigs (Yorkshire × Landrace × Duroc) weighing 6.49 ± 0.08 kg were randomly divided into five treatments with eight replicates each. The pre-feeding period was 2 days, followed by a 22-day experimental period. All groups were exposed to high heat conditions at 35 ± 1 °C. The control group received a basal diet, while the low copper sulfate (LC) group received a diet with 50 mg/kg of copper sulfate, the high copper sulfate (HC) group received a diet with 150 mg/kg of copper sulfate, the low nano-copper (LNC) group received a diet with 50 mg/kg of nano-copper oxide, and the high nano-copper (HNC) group received a diet with 150 mg/kg of nano-copper oxide. Compared to the basal group, pigs supplemented with copper (either CuSO₄ or nano-CuO) exhibited significantly higher average daily gain (ADG, p < 0.048) and feed intake (ADFI, p = 0.005), with the 50 mg/kg nano-copper group showing improved nutrient digestibility (p < 0.05) and intestinal morphology. Nano-copper supplementation significantly enhanced mucosal SOD activity (p < 0.05), reduced MDA levels (p < 0.05), and downregulated pro-inflammatory cytokines such as IL-1β and IL-6 (p < 0.05). Notably, 50 mg/kg of nano-copper increased the apparent total tract digestibility (ATTD) of copper to 30.29%, significantly higher than the 16.55% observed in the 150 mg/kg CuSO₄ group (p < 0.05). Furthermore, fecal copper concentration was significantly reduced by 20.7% in the 50 mg/kg nano-copper group compared to copper sulfate (p < 0.001). In conclusion, nano-copper appears to be a promising alternative to copper sulfate for improving growth performance and reducing fecal copper concentrations in weanling pigs under heat stress conditions. Full article

(This article belongs to the Section Farm Animal Production)

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Agriculture, Volume 15, Issue 12 (June-2 2025) – 92 articles

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