Agriculture

24 pages, 1092 KiB

Open AccessArticle

The Impact of Federal Reserve Monetary Policy on Commodity Prices: Evidence from the U.S. Dollar Index and International Grain Futures and Spot Markets

by Xuezhen Ba, Xizhao Wang and Yu Zhong

Agriculture 2025, 15(9), 923; https://doi.org/10.3390/agriculture15090923 - 23 Apr 2025

Abstract

There is a strong connection between the Federal Reserve’s monetary policy and the trend of international food prices. Employing the average information share model, EGARCH(Exponential Generalized Autoregressive Conditional Heteroskedasticity), and DCC-MGARCH(Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity) models, this study investigates the relationship [...] Read more.

There is a strong connection between the Federal Reserve’s monetary policy and the trend of international food prices. Employing the average information share model, EGARCH(Exponential Generalized Autoregressive Conditional Heteroskedasticity), and DCC-MGARCH(Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity) models, this study investigates the relationship between the U.S. dollar index, international grain futures prices, and spot prices in the context of Federal Reserve monetary policy adjustments from 2000 to 2023. The findings reveal that, first, under conditions of long-run cointegration, the U.S. dollar index exerts a strong pricing influence over international grain futures, while grain futures demonstrate a significant price discovery function over spot prices. Second, both international grain futures and spot markets exhibit asymmetric volatility, with price increases being more pronounced than decreases in response to external shocks. Additionally, the U.S. dollar index has a unidirectional and inverse influence on grain futures prices, while futures and spot prices interact bidirectionally and move in the same direction. This paper contributes to understanding the impact of Federal Reserve monetary policy adjustments on international food prices and offers policy insights for countries to manage food import risks and maintain price stability. Full article

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

23 pages, 4051 KiB

Open AccessArticle

Parameter Optimization and Experimental Study on Alfalfa Stem Flattening Process Based on DEM–MBD

by Zhikai Yang, Keping Zhang, Jinlong Yang and Yaping Yao

Agriculture 2025, 15(9), 922; https://doi.org/10.3390/agriculture15090922 - 23 Apr 2025

Abstract

To address issues such as uneven flattening and high stem breakage rate in post-harvest alfalfa field conditioning operations, an adjustable-clearance flattening and modulating device was designed. The device incorporates a dual-spring floating pressure mechanism and preload adjustment mechanism to ensure the adaptive performance [...] Read more.

To address issues such as uneven flattening and high stem breakage rate in post-harvest alfalfa field conditioning operations, an adjustable-clearance flattening and modulating device was designed. The device incorporates a dual-spring floating pressure mechanism and preload adjustment mechanism to ensure the adaptive performance of conditioning rollers during alfalfa stem flattening. Based on the biological characteristics of alfalfa stems, a rigid–flexible coupling model between stems and the flattening and modulating device was established. Using the Discrete Element Method (DEM) and Multibody Dynamics (MBD) co-simulation technology, experiments were conducted with feeding amount, roller speed, and buffer spring preload force as test factors, while stem crushing rate and bonding key fracture rate served as evaluation indices. Box–Behnken experimental design was employed to simulate the dynamic conditioning process, followed by regression analysis of the simulation results. The findings revealed optimal parameter combinations as follows: feeding amount of 5.10 kg/s, modulation roller speed of 686.87 r/min, and buffer spring preload force of 670.02 N. According to the optimal combination of parameters to carry out field tests, the average flattening rate of stem and stem crushing rate were 95.71% and 1.73%, respectively, which showed small relative error with the predicted value and met the requirements of alfalfa steam flattening and modulation operation. These research findings provide theoretical basis and technical support for the design and optimization of alfalfa flattening and modulating devices. Full article

(This article belongs to the Section Agricultural Technology)

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24 pages, 14024 KiB

Open AccessArticle

Biomimetic Structural Design for Reducing the Adhesion Between Wet Rice Leaves and Metal Surfaces

by Pengfei Qian, Qi He, Zhong Tang and Tingwei Gu

Agriculture 2025, 15(9), 921; https://doi.org/10.3390/agriculture15090921 - 23 Apr 2025

Abstract

Adhesion behavior between wet rice leaves and metal surfaces exacerbates the difficulty in separating and removing grains in the cleaning device. Reducing the adhesion between the wet rice leaves and the cleaning device is an important factor in improving the harvesting performance of [...] Read more.

Adhesion behavior between wet rice leaves and metal surfaces exacerbates the difficulty in separating and removing grains in the cleaning device. Reducing the adhesion between the wet rice leaves and the cleaning device is an important factor in improving the harvesting performance of rice combine harvesters. This paper investigates the possibility of reducing the adhesion between them. By studying the liquid shape characteristics between the removed grains and the surface, it was found that the adhesion force between the leaf and the surface is greatest when additional pressure is present. Based on biomimetic principles and the convex hull structure of a dung beetle’s head, a convex hull structure for the metal surface was designed to balance the atmospheric pressure on both sides of the leaf in order to eliminate additional pressure. Using the liquid bridge model between a spherical and a flat surface, a liquid bridge model for the leaf and convex hull surface was established. By optimizing the minimum liquid bridge force, the convex hull radius and distance were determined to be 2.47 mm and 1.38 mm, respectively. Contact and collision experiments verified that the convex hull surface is more effective in reducing the adhesion of moist leaves, providing a reference for future research on the cleaning methods of moist rice grains. Full article

(This article belongs to the Section Agricultural Technology)

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17 pages, 7190 KiB

Open AccessArticle

Optimising Farm Area Allocations Based on Soil Moisture Thresholds: A Comparative Study of Two Dairy Farms with Distinct Soil and Topographic Features

by Rumia Basu, Owen Fenton, Gourav Misra and Patrick Tuohy

Agriculture 2025, 15(9), 920; https://doi.org/10.3390/agriculture15090920 - 23 Apr 2025

Abstract

On intensive dairy farms, good decision making regarding application of fertilisers and irrigation requires an understanding of soil moisture conditions. Targeted fertiliser application not only contributes to high nutrient use efficiency but reduces the potential for leaching of nutrients and controls emissions from [...] Read more.

On intensive dairy farms, good decision making regarding application of fertilisers and irrigation requires an understanding of soil moisture conditions. Targeted fertiliser application not only contributes to high nutrient use efficiency but reduces the potential for leaching of nutrients and controls emissions from farms. This calls for the development of an improved farm management decision support system focussed on precision agriculture solutions for sustainable agriculture. Knowledge of soil moisture at high resolution at the farm scale can help develop such solutions while at the same time reducing the risk of soil compaction by machinery and/or animals, especially under wet conditions. The objective of this study is to examine and compare two intensive dairy farms, with similar average annual rainfall but contrasting soil (but similar drainage) and topographic characteristics, for their resilience towards extreme conditions (e.g., saturation or drought). Soil moisture thresholds for optimal conditions and corresponding farm area proportions were calculated, identifying areas for targeted farm management. This study addresses the knowledge gap of including high-resolution satellite derived soil moisture as a variable in designing farm management systems targeted towards precision agriculture. Farm 1 was situated in a drumlin belt, whereas Farm 2 had lowland terrain, representing major land cover categories in Ireland. The results showed that Farm 2 was more resilient towards extreme conditions and that the variable topography and soil heterogeneity act as a buffer in regulating moisture regimes on the farm, preventing movement towards the extremes. Across the years, Farm 1 showed less variability in optimal farm area proportions and could be managed better than Farm 2 in terms of overall productivity and resilience towards extreme weather conditions such as droughts, even in a drought year. This study showed that along with variations in soil type, topographic features also dictate water movement and therefore soil moisture regimes on farms. Full article

(This article belongs to the Section Agricultural Water Management)

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27 pages, 4152 KiB

Open AccessArticle

A Hybrid Model Integrating Variational Mode Decomposition and Intelligent Optimization for Vegetable Price Prediction

by Gao Wang, Shuang Xu, Zixu Chen and Youzhu Li

Agriculture 2025, 15(9), 919; https://doi.org/10.3390/agriculture15090919 - 23 Apr 2025

Abstract

In recent years, China’s vegetable market has faced frequent and drastic price fluctuations due to factors such as supply–demand relationships and climate change, which significantly affect government bodies, farmers, consumers, and other participants in the vegetable industry and supply chain. Traditional forecasting methods [...] Read more.

In recent years, China’s vegetable market has faced frequent and drastic price fluctuations due to factors such as supply–demand relationships and climate change, which significantly affect government bodies, farmers, consumers, and other participants in the vegetable industry and supply chain. Traditional forecasting methods demonstrate evident limitations in capturing the nonlinear characteristics and complex volatility patterns of price series, underscoring the necessity of developing high-precision prediction models. This study proposes a hybrid forecasting model integrating variational mode decomposition (VMD), the Fruit Fly Optimization Algorithm (FOA), and a gated recurrent unit (GRU). The model employs VMD for multi-scale decomposition of original price series and utilizes the FOA for adaptive optimization of the GRU’s critical parameters, effectively addressing the challenges of high volatility and nonlinearity in agricultural price forecasting. Empirical analysis conducted on daily price data of six major vegetables, specifically, Chinese cabbage, cucumber, beans, tomato, chili, and radish, from 2014 to 2024 reveals that the proposed model significantly outperforms traditional methods, single deep learning models, and other hybrid models in predictive performance. Experimental results indicate substantial improvements in key metrics including the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²), with R² values consistently exceeding 99.4% and achieving over 5% enhancement compared to the baseline GRU model. This research establishes a novel methodological framework for analyzing agricultural price forecasting while providing reliable technical support for market monitoring and policy regulation. Full article

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

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15 pages, 7102 KiB

Open AccessArticle

Non-Contact Detection of Wine Grape Load Volume in Hopper During Mechanical Harvesting

by Haowei Liu, Xiu Wang, Jian Song, Mingzhou Chen, Cuiling Li and Changyuan Zhai

Agriculture 2025, 15(9), 918; https://doi.org/10.3390/agriculture15090918 - 23 Apr 2025

Abstract

Issues of poor real-time performance and low accuracy in the detection of load volume in the hopper during the mechanized harvesting of wine grapes are addressed in this study through the development of a proposed volume detection method based on ultrasonic sensors. First, [...] Read more.

Issues of poor real-time performance and low accuracy in the detection of load volume in the hopper during the mechanized harvesting of wine grapes are addressed in this study through the development of a proposed volume detection method based on ultrasonic sensors. First, the ultrasonic sensor beamwidth and detection height were determined through calibration tests. Next, a test bench was used to explore the influence of the number of ultrasonic sensors and conveying speed on the detected grape pile height. Data-based regression and hopper configuration-based geometric models correlating grape load volume with detected pile height were subsequently constructed; their accuracies were compared using test bench experiments to identify the optimal detection scheme. The regression model was more accurate than the geometric model under the considered conveying speeds with a maximum relative error of 8.0% for the former. Finally, field tests determined that the average grape load volume detection error during actual harvesting was 14.4%. Therefore, this study provides an effective solution for the detection of grape load volume in the hopper during mechanized harvesting and establishes a theoretical basis for the development of intelligent grape harvesting methods. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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15 pages, 4298 KiB

Open AccessArticle

Response of Different Perennial Ryegrass Varieties to Water Stress

by Mladen Prijović, Dejan Sokolović, Jelena Dragišić Maksimović, Vuk Maksimović, Dragica Milosavljević, Snežana Babić, Marija Stepić and Aneta Sabovljević

Agriculture 2025, 15(9), 917; https://doi.org/10.3390/agriculture15090917 - 22 Apr 2025

Abstract

Perennial ryegrass represents the most important forage grass, yet its generally low drought tolerance leads to reduced yields under water scarcity. Nevertheless, large intra- and inter-population variability could be a pool for selecting new drought-tolerant varieties. In this study we evaluated three populations [...] Read more.

Perennial ryegrass represents the most important forage grass, yet its generally low drought tolerance leads to reduced yields under water scarcity. Nevertheless, large intra- and inter-population variability could be a pool for selecting new drought-tolerant varieties. In this study we evaluated three populations (K-11, Exp population and Shandon) under semi-controlled conditions across four watering levels (100%, 70%, 50% and 30% of field water capacity), focusing on yield and key morphological and biochemical traits. Dry matter yield and root dry mass decreased in all populations under limited watering conditions. The highest biomass production in such conditions was observed in the Exp population, likely due to better root performance in the deeper soil layer. On the other hand, oxidative stress markers (MDA and H₂O₂) and water-soluble sugars, which indicated the best physiological status in cultivar K-11 under severe drought, did not lead to the highest DMY. These results show the importance of including multiple physiological and biochemical traits in breeding processes, with the aim of developing perennial ryegrass cultivars capable of withstanding prolonged and intense summer drought as a consequence of climate change. Full article

(This article belongs to the Topic Advances in Water and Soil Management Towards Climate Change Adaptation)

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29 pages, 12039 KiB

Open AccessArticle

The Design and Testing of a Combined Operation Machine for Corn Straw Crushing and Residual Film Recycling

by Jiuxin Wang, Wuyun Zhao, Xiaolong Liu, Fei Dai, Ruijie Shi, Keping Zhang, Xiaoyang Wang, Wenhui Zhang and Jiadong Liang

Agriculture 2025, 15(9), 916; https://doi.org/10.3390/agriculture15090916 - 22 Apr 2025

Abstract

To address the negative impacts in recovering large areas of residual plastic film from corn stubble in the Hexi irrigation area—such as the residual film containing substantial amounts of soil, corn stubble, and corn straw, and high power consumption during the operation process—in [...] Read more.

To address the negative impacts in recovering large areas of residual plastic film from corn stubble in the Hexi irrigation area—such as the residual film containing substantial amounts of soil, corn stubble, and corn straw, and high power consumption during the operation process—in this study, a combined operation machine was designed for corn straw crushing and residual film recovery. The machine consisted of a double-wing, single-blade shovel for lifting the film and cutting corn stubble, a corn straw-crushing and returning device for reducing the residual film impurity rate, an eccentric teeth shifting cylinder for picking up residual film, a device for shifting residual film, and a collection device for bundling residual film. The key components of the combined operation machine were designed based on an agronomic model for corn planting and the mechanized operation requirements in the Hexi irrigation area. The optimal combination of operating parameters was devised based on theoretical calculations and single- and multifactor simulation tests. The results showed that when the angle of entry of the film-lifting shovel was 25.14°, the rotational speed of the eccentric teeth shifting cylinder was 80.96 rpm, and the forward velocity of the machine was 4.03 km/h, while the rate of recovery of residual film was 92.56%. The field test showed that the residual film contained 16.65% impurities, and the qualified rate of corn straw crushing was 88.51%, with a relative error of 0.65% from the optimized value. The experimental results provide theoretical support and a design reference for research on the mechanized recycling of residual film in large areas of corn stubble. Full article

(This article belongs to the Section Agricultural Technology)

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13 pages, 920 KiB

Open AccessArticle

Biosolarization and Chemical Disinfection as Strategies to Enhance Asparagus Yield and Quality in a Decline-Affected Plantation

by Francisco Javier López-Moreno, Eloy Navarro-León, Juan Manuel Ruiz and Teresa Soriano

Agriculture 2025, 15(9), 915; https://doi.org/10.3390/agriculture15090915 - 22 Apr 2025

Abstract

Asparagus decline syndrome (ADS) is a major challenge affecting asparagus production, leading to reduced yield and spear quality. This study evaluated the effectiveness of different control strategies, including biosolarization with Brassica carinata seed pellets, biosolarization with chicken manure pellets, and chemical disinfection with [...] Read more.

Asparagus decline syndrome (ADS) is a major challenge affecting asparagus production, leading to reduced yield and spear quality. This study evaluated the effectiveness of different control strategies, including biosolarization with Brassica carinata seed pellets, biosolarization with chicken manure pellets, and chemical disinfection with Dazomet. Field trials were conducted over three consecutive years to assess their impact on commercial yield, spear quality, and plant performance. Biosolarization with B. carinata seed pellets increased commercial yield by 17% and the number of spears per plot by 21%, compared to the control. B. carinata seed pellets and Dazomet improved spear weight by 196% and 170%, respectively, and increased diameter by 115% and 95%, respectively, in 2019. In 2021, chicken manure pellets and Dazomet treatments reduced hardness by 11% and °Brix by 5% and 4%, respectively. These findings suggest that biosolarization could be an effective strategy to mitigate ADS effects and enhance asparagus yield and quality. Furthermore, the results highlight the importance of considering biological control methods to manage ADS while preserving beneficial soil microorganisms. This study provides valuable insights for sustainable asparagus production, emphasizing the role of biosolarization as an alternative to chemical disinfection in ADS-affected fields. Full article

(This article belongs to the Section Crop Production)

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24 pages, 1965 KiB

Open AccessArticle

Socioeconomic Factors Influencing Crop Diversification Among Smallholder Farmers in Bergville, South Africa

by Busisiwe Vilakazi, Alfred O. Odindo, Mutondwa M. Phophi and Paramu L. Mafongoya

Agriculture 2025, 15(9), 914; https://doi.org/10.3390/agriculture15090914 - 22 Apr 2025

Abstract

Crop diversification is a vital strategy for achieving sustainable agriculture and food security, yet adoption rates remain low. This study examined the socioeconomic factors influencing crop diversification among smallholder farmers. A two-stage sampling procedure was employed to elicit data from 161 farmers solely [...] Read more.

Crop diversification is a vital strategy for achieving sustainable agriculture and food security, yet adoption rates remain low. This study examined the socioeconomic factors influencing crop diversification among smallholder farmers. A two-stage sampling procedure was employed to elicit data from 161 farmers solely specializing in crop production. A structured questionnaire was used to collect data, analyzed using descriptive statistics. The multiple linear regression and multivariate probit regression models were applied to assess the socioeconomic factors influencing diversification. The results revealed that smallholders primarily focused on vegetable cultivation (87%), followed by cereals (56%) and legumes (43%). Education level, household size, market access, and the perceived benefits of diversification significantly (p < 0.05) influenced diversification decisions. Also, sources of irrigation water, age, marital status, and farm size were key factors in vegetable diversification, while farming experience, farm size, and perceived benefits influenced legume diversification. Only marital status and farming experience were positively linked to cereal crop diversification. Furthermore, 48.4% of farmers practice intercropping, integrating maize with pumpkins or sugar beans, while 33.5% still rely on monoculture, predominantly maize, due to limited resources. These findings highlight the need for policies and extension support to address socioeconomic barriers and encourage a wider adoption of crop diversification strategies. Full article

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

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17 pages, 14016 KiB

Open AccessArticle

Geospatial Analysis, Source Apportionment, and Ecological–Health Risks Assessment of Topsoil Heavy Metal(loid)s in a Typical Agricultural Area

by Denghui Wei, Shiming Yang, Haidong Li, Ming Luo, Ying Wang, Yangshuang Wang, Yunhui Zhang and Bin Wang

Agriculture 2025, 15(9), 913; https://doi.org/10.3390/agriculture15090913 - 22 Apr 2025

Abstract

Soil environmental protection has become a pressing issue for sustainable development. This study collected 153 topsoil samples from a typical agricultural area to evaluate the contamination characteristics of heavy metal(loid)s (HMs), identify their potential sources, and assess the associated ecological and human health [...] Read more.

Soil environmental protection has become a pressing issue for sustainable development. This study collected 153 topsoil samples from a typical agricultural area to evaluate the contamination characteristics of heavy metal(loid)s (HMs), identify their potential sources, and assess the associated ecological and human health risks. The results showed that the mean concentration of HMs was in the order of Zn > Cr > Ni > Pb > Cu > As > Cd > Hg; all HMs were below their background levels, except Cd. The geo-accumulation index (Igeo) and improved Nemerow index (INI) revealed that the overall pollution level was considered as no or slight contamination, while HMs posed low ecological risk according to the ecological hazard factor (E_i) and potential ecological risk index (PERI). In addition, three main sources were identified through the positive matrix factorization (PMF) model: natural source (48.2%; contributed As, Ni, Cu, Pb, and Zn), coal burning and waste disposal (24%; contributed Hg and Cd), and agricultural activities (27.8%; contributed Cr). The human health risk (HHR) assessment model and Monte Carlo simulation were applied to evaluate human health risks, and the results suggested that children faced higher health risks than adults, with 45.83% of samples exceeding the non-carcinogenic acceptable limit. As and Cr were the main contributors to non-carcinogenic and carcinogenic risks, respectively. The findings contributed to the local environmental management and sustainable development of agriculture. Full article

(This article belongs to the Special Issue From Agricultural Soils to Human Health: Exposure Sources, Intake Pathways, and Accumulation of Heavy Metals)

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19 pages, 16379 KiB

Open AccessArticle

Using Satellites to Monitor Soil Texture in Typical Black Soil Areas and Assess Its Impact on Crop Growth

by Liren Gao, Yuhong Zhang, Deqiang Zang, Qian Yang, Huanjun Liu and Chong Luo

Agriculture 2025, 15(9), 912; https://doi.org/10.3390/agriculture15090912 - 22 Apr 2025

Abstract

Soil texture is an important physical property of soil. Understanding the spatial distribution of cultivated soil texture in black soil areas is crucial for precise agricultural management and cultivated land protection in these zones. This study utilizes the random forest algorithm, Landsat-8 satellite [...] Read more.

Soil texture is an important physical property of soil. Understanding the spatial distribution of cultivated soil texture in black soil areas is crucial for precise agricultural management and cultivated land protection in these zones. This study utilizes the random forest algorithm, Landsat-8 satellite remote sensing data, and climate- and terrain-related environmental covariates to map the spatial distribution of soil texture and analyze its impact on crop growth. The results show that (1) the order of prediction accuracy differs for different soil texture types; April is determined to have the highest prediction accuracy for silt and sand, while May exhibits the greatest accuracy for clay. (2) Adding environmental variables can significantly improve the accuracy of soil texture predictions; the root mean square error (RMSE) has decreased to varying degrees (silt: 0.84; clay: 0.04; sand: 0.85). (3) Soybean growth has the strongest response to soil texture; clay grain is the key factor affecting crop growth in drought scenarios, and sand grain is the dominant factor influencing flooding. This study improves the accuracy of the remote sensing mapping of soil texture through the combination of remote sensing images and environmental variables and quantitatively evaluates the impact of soil texture on crop growth. Full article

(This article belongs to the Section Digital Agriculture)

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5 pages, 1517 KiB

Open AccessCorrection

Correction: Liao et al. A Lightweight Cotton Verticillium Wilt Hazard Level Real-Time Assessment System Based on an Improved YOLOv10n Model. Agriculture 2024, 14, 1617

by Juan Liao, Xinying He, Yexiong Liang, Hui Wang, Haoqiu Zeng, Xiwen Luo, Xiaomin Li, Lei Zhang, He Xing and Ying Zang

Agriculture 2025, 15(9), 911; https://doi.org/10.3390/agriculture15090911 - 22 Apr 2025

Abstract

In the original publication [...] Full article

(This article belongs to the Section Digital Agriculture)

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17 pages, 5873 KiB

Open AccessArticle

Soil Organic Carbon Monitoring and Modelling via Machine Learning Methods Using Soil and Remote Sensing Data

by Dimitrios Triantakonstantis and Andreas Karakostas

Agriculture 2025, 15(9), 910; https://doi.org/10.3390/agriculture15090910 - 22 Apr 2025

Abstract

(1) Background: Soil organic carbon (SOC) is an important parameter of soils and a critical factor in global carbon cycling. The accurate monitoring and modelling of SOC are essential for assessing soil fertility, facilitating sustainable land management, and mitigating climate change. (2) Methods: [...] Read more.

(1) Background: Soil organic carbon (SOC) is an important parameter of soils and a critical factor in global carbon cycling. The accurate monitoring and modelling of SOC are essential for assessing soil fertility, facilitating sustainable land management, and mitigating climate change. (2) Methods: This research paper explores the integration of machine learning (ML) approaches with soil, terrain and remotely sensed data to enhance SOC estimation. Various ML models, including Neural Networks (NNs), Random Forests (RFs), Support Vector Machines (SVMs) and Decision Trees (DTs), were trained and evaluated using a dataset comprising soil laboratory data, Sentinel-2 spectral indices, climate data and topographic features. Feature selection techniques were applied to indicate the most important predictors, improving model performance and interpretability. (3) Results: The results demonstrate the potential of ML-driven approaches to achieve high accuracy in SOC prediction. (4) Conclusions: This research highlights the advantages of leveraging big data and artificial intelligence in soil monitoring, providing a scalable and cost-effective framework for SOC assessment in agricultural and environmental applications. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Soil Quality Assessment)

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12 pages, 797 KiB

Open AccessReview

Unleashing the Potential of Urban Agroecology to Reach Biodiversity Conservation, Food Security and Climate Resilience

by Miguel A. Altieri, Angel Salazar-Rojas, Clara I. Nicholls and Andrea Giacomelli

Agriculture 2025, 15(9), 909; https://doi.org/10.3390/agriculture15090909 - 22 Apr 2025

Abstract

Urban agriculture is considered by many scientists and policymakers as a key strategy to build climate change-resilient communities within cities by strengthening food systems, with positive food security, biodiversity, nutrition and health outcomes. The estimated potential of urban agriculture to provide between 15 [...] Read more.

Urban agriculture is considered by many scientists and policymakers as a key strategy to build climate change-resilient communities within cities by strengthening food systems, with positive food security, biodiversity, nutrition and health outcomes. The estimated potential of urban agriculture to provide between 15 and 20% of the global food supply can be enhanced by applying agroecological principles and practices that revitalize urban agriculture cropping systems, thus leading to the design of highly diversified, productive and resilient urban farms on a planet in polycrisis. Two pillars are used in agroecology: (a) restoring spatial and temporal crop combinations that deter pests by enhancing biological control with natural enemies, and (b) increasing soil organic matter through green manures, compost and other organic practices that enhance soil fertility and beneficial microorganisms. In addition to technical and environmental obstacles, there are a series of social, economic and political barriers that limit the scaling-up of urban agriculture. For this reason, it is important to launch policies that establish mechanisms for cities to provide incentives for urban agriculture, including access to land, water, seeds and technical knowledge. The creation of producer–consumer networks around markets with solidarity is critical for local equitable food provision and consumption. Full article

(This article belongs to the Special Issue Current Prospects of Social-Ecologically More Sustainable Agriculture and Urban Agriculture)

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25 pages, 631 KiB

Open AccessReview

A Comprehensive Review of Digital Twins Technology in Agriculture

by Ruixue Zhang, Huate Zhu, Qinglin Chang and Qirong Mao

Agriculture 2025, 15(9), 903; https://doi.org/10.3390/agriculture15090903 - 22 Apr 2025

Abstract

Digital Twin (DT) technology has emerged as a transformative tool in various sectors, like agriculture, due to its potential to improve productivity, sustainability, and decision making processes. This paper provides a comprehensive review of the applications, challenges, and future directions of DT technology [...] Read more.

Digital Twin (DT) technology has emerged as a transformative tool in various sectors, like agriculture, due to its potential to improve productivity, sustainability, and decision making processes. This paper provides a comprehensive review of the applications, challenges, and future directions of DT technology in agriculture. We explore the key concepts and architecture of DTs, focusing on the layering and classification of DT systems. The review delves into the various applications of DTs, such as crop planting management, pest and disease control, livestock management, optimization of agricultural machinery and resource, and agricultural decision support systems. Furthermore, we highlight the integration of agricultural data acquisition, simulation, and modeling techniques that form the backbone of effective DT implementation. Despite its promising potential, the adoption of DTs in agriculture faces several technical challenges, including data acquisition issues, integration difficulties, and the standardization of 3D crop models. Finally, we discuss future direction of DT technology, emphasizing the importance of overcoming existing barriers for wider application and sustainability. Full article

(This article belongs to the Section Digital Agriculture)

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Agriculture, Volume 15, Issue 9 (May-1 2025) – 16 articles

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