Hasil untuk "Agriculture"

Fernanda Alberto, M. S. Guerreiro

Agricultural Ecosystems Facts and Trends Foreword The rapid increase in food prices in many countries has led to substantial media attention on agriculture. We hope that providing this Facts & Trends publication will help uncover some key trade-offs that mean extremely challenging decisions for governments, farmers, consumers and industry. This publication does not attempt to cover everything there is to know about agricultural ecosystems. Rather, it tries to present well-documented facts and fi gures to better understand the challenges facing the sustainable management of agricultural ecosystems. We have used existing data from the World Resources Institute (WRI), the World Bank, the Food and Agriculture Organization of the United Nations (FAO), the Intergovernmental Panel on Climate Change (IPCC) and many other sources. We present it here in a simplifi ed and condensed format to stimulate forward thinking and ongoing dialogue between business, civil society, government and other stakeholders.

A. Raza, Ali Razzaq, S. Mehmood et al.

Agriculture and climate change are internally correlated with each other in various aspects, as climate change is the main cause of biotic and abiotic stresses, which have adverse effects on the agriculture of a region. The land and its agriculture are being affected by climate changes in different ways, e.g., variations in annual rainfall, average temperature, heat waves, modifications in weeds, pests or microbes, global change of atmospheric CO2 or ozone level, and fluctuations in sea level. The threat of varying global climate has greatly driven the attention of scientists, as these variations are imparting negative impact on global crop production and compromising food security worldwide. According to some predicted reports, agriculture is considered the most endangered activity adversely affected by climate changes. To date, food security and ecosystem resilience are the most concerning subjects worldwide. Climate-smart agriculture is the only way to lower the negative impact of climate variations on crop adaptation, before it might affect global crop production drastically. In this review paper, we summarize the causes of climate change, stresses produced due to climate change, impacts on crops, modern breeding technologies, and biotechnological strategies to cope with climate change, in order to develop climate resilient crops. Revolutions in genetic engineering techniques can also aid in overcoming food security issues against extreme environmental conditions, by producing transgenic plants.

J. Sanderman, T. Hengl, G. Fiske

Subrahmaniyan Kasirajan, M. Ngouajio

The use of plastic mulch in agriculture has increased dramatically in the last 10 years throughout the world. This increase is due to benefits such as increase in soil temperature, reduced weed pressure, moisture conservation, reduction of certain insect pests, higher crop yields, and more efficient use of soil nutrients. However, disposing of used plastic films, which cause pollution, has led to development of photodegradable and biodegradable mulches. Here we review the use of plastic mulches in agriculture, with special reference to biodegradable mulches. Major topics discussed are (1) history of plastic mulch and impact on crop yield and pest management, (2) limitations of polyethylene mulches and potential alternatives, (3) biodegradable and photodegradable plastic mulches, (4) field performance of biodegradable mulches, and (5) use of biodegradable plastic mulches in organic production. We found that (1) despite multiple benefits, removal and disposal of conventional polyethylene mulches remains a major agronomic, economic, and environmental constraint; (2) early use of photodegradable plastic mulch during the 1970s and 1980s, wrongly named biodegradable mulch films, discouraged adoption of new biodegradable mulch films because they were too expensive and their breakdown was unpredictable; (3) biodegradable plastic films are converted through microbial activity in the soil to carbon dioxide, water, and natural substances; (4) polymers such as poly(lactic acid), poly(butylene adipate-coterephthalate), poly(ε-caprolactone), and starch-based polymer blends or copolymers can degrade when exposed to bioactive environments such as soil and compost; (5) with truly biodegradable materials obtained from petroleum and natural resources, opportunity for using biodegradable polymers as agricultural mulch films has become more viable; and (6) the source of polymer and additives may limit use of some biodegradable mulches in organic production. More knowledge is needed on the effect of biodegradable mulches on crop growth, microclimate modifications, soil biota, soil fertility, and yields.

B. Phalan, Malvika Onial, A. Balmford et al.

A. Wezel, S. Bellon, T. Doré et al.

Agroecology involves various approaches to solve actual challenges of agricultural production. Though agroecology initially dealt primarily with crop production and protection aspects, in recent decades new dimensions such as environmental, social, economic, ethical and development issues are becoming relevant. Today, the term ‘agroecology’ means either a scientific discipline, agricultural practice, or political or social movement. Here we study the different meanings of agroecology. For that we analyse the historical development of agroecology. We present examples from USA, Brazil, Germany, and France. We study and discuss the evolution of different meanings agroecology. The use of the term agroecology can be traced back to the 1930s. Until the 1960s agroecology referred only as a purely scientific discipline. Then, different branches of agroecology developed. Following environmental movements in the 1960s that went against industrial agriculture, agroecology evolved and fostered agroecological movements in the 1990s. Agroecology as an agricultural practice emerged in the 1980s, and was often intertwined with movements. Further, the scales and dimensions of agroecological investigations changed over the past 80 years from the plot and field scales to the farm and agroecosystem scales. Actually three approaches persist: (1) investigations at plot and field scales, (2) investigations at the agroecosystem and farm scales, and (3) investigations covering the whole food system. These different approaches of agroecological science can be explained by the history of nations. In France, agroecology was mainly understood as a farming practice and to certain extent as a movement, whereas the corresponding scientific discipline was agronomy. In Germany, agroecology has a long tradition as a scientific discipline. In the USA and in Brazil all three interpretations of agroecology occur, albeit with a predominance of agroecology as a science in the USA and a stronger emphasis on movement and agricultural practice in Brazil. These varied meanings of the term agroecology cause confusion among scientists and the public, and we recommend that those who publish using this term be explicit in their interpretation.

P. Vitousek, R. Naylor, T. Crews et al.

E. Fereres, M. Soriano

J. Olesen, M. Bindi

Jeffrey G. Arnold, N. Fohrer

D. Pimentel, P. Hepperly, J. Hanson et al.

G. Samuelson

AbstractAn expert report aiming to design strategies in promoting healthy diets and physical activity behaviours was published a year ago by the United Nations Food and Agriculture Organization (FA...

Mei Ling Chee, Thangarajah Akilan, Aparna Ravindra Phalke et al.

Semantic segmentation in high-resolution agricultural imagery demands models that strike a careful balance between accuracy and computational efficiency to enable deployment in practical systems. In this work, we propose DAS-SK, a novel lightweight architecture that retrofits selective kernel convolution (SK-Conv) into the dual atrous separable convolution (DAS-Conv) module to strengthen multi-scale feature learning. The model further enhances the atrous spatial pyramid pooling (ASPP) module, enabling the capture of fine-grained local structures alongside global contextual information. Built upon a modified DeepLabV3 framework with two complementary backbones - MobileNetV3-Large and EfficientNet-B3, the DAS-SK model mitigates limitations associated with large dataset requirements, limited spectral generalization, and the high computational cost that typically restricts deployment on UAVs and other edge devices. Comprehensive experiments across three benchmarks: LandCover.ai, VDD, and PhenoBench, demonstrate that DAS-SK consistently achieves state-of-the-art performance, while being more efficient than CNN-, transformer-, and hybrid-based competitors. Notably, DAS-SK requires up to 21x fewer parameters and 19x fewer GFLOPs than top-performing transformer models. These findings establish DAS-SK as a robust, efficient, and scalable solution for real-time agricultural robotics and high-resolution remote sensing, with strong potential for broader deployment in other vision domains.

Dmitrii Usenko, David Helman, Chen Giladi

Accurate estimation of total leaf area (TLA) is crucial for evaluating plant growth, photosynthetic activity, and transpiration. However, it remains challenging for bushy plants like dwarf tomatoes due to their complex canopies. Traditional methods are often labor-intensive, damaging to plants, or limited in capturing canopy complexity. This study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA for three dwarf tomato cultivars: Mohamed, Hahms Gelbe Topftomate, and Red Robin -- grown under controlled greenhouse conditions. Two experiments (spring-summer and autumn-winter) included 73 plants, yielding 418 TLA measurements via an "onion" approach. High-resolution videos were recorded, and 500 frames per plant were used for 3D reconstruction. Point clouds were processed using four algorithms (Alpha Shape, Marching Cubes, Poisson's, Ball Pivoting), and meshes were evaluated with seven regression models: Multivariable Linear Regression, Lasso Regression, Ridge Regression, Elastic Net Regression, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron. The Alpha Shape reconstruction ($α= 3$) with Extreme Gradient Boosting achieved the best performance ($R^2 = 0.80$, $MAE = 489 cm^2$). Cross-experiment validation showed robust results ($R^2 = 0.56$, $MAE = 579 cm^2$). Feature importance analysis identified height, width, and surface area as key predictors. This scalable, automated TLA estimation method is suited for urban farming and precision agriculture, offering applications in automated pruning, resource efficiency, and sustainable food production. The approach demonstrated robustness across variable environmental conditions and canopy structures.

Mohd Ruhul Ameen, Akif Islam, Farjana Aktar et al.

In Bangladesh, many farmers still struggle to access timely, expert-level agricultural guidance. This paper presents KrishokBondhu, a voice-enabled, call-centre-integrated advisory platform built on a Retrieval-Augmented Generation (RAG) framework for Bengali-speaking farmers. The system combines agricultural handbooks, extension manuals, and NGO publications, processes them through an OCR-based pipeline, and indexes the curated content in a vector database for semantic retrieval. Through a phone-based interface, farmers can receive real-time, context-aware advice: speech-to-text converts the Bengali query, the RAG module retrieves relevant information, a large language model (Gemma 3-4B) generates a grounded response, and text-to-speech delivers the answer in spoken Bengali. In a pilot evaluation, KrishokBondhu produced high-quality responses for 72.7% of diverse agricultural queries. Compared to the KisanQRS benchmark, it achieved a composite score of 4.53 versus 3.13 on a 5-point scale, with a 44.7% improvement and especially large gains in contextual richness and completeness, while maintaining comparable relevance and technical specificity. Semantic-similarity analysis further showed a strong correlation between retrieved context and answer quality. KrishokBondhu demonstrates the feasibility of combining call-centre accessibility, multilingual voice interaction, and modern RAG techniques to deliver expert-level agricultural guidance to remote Bangladeshi farmers.

Mahsa Khosravi, Zhanhong Jiang, Joshua R Waite et al.

This paper presents a novel reinforcement learning (RL)-based planning scheme for optimized robotic management of biotic stresses in precision agriculture. The framework employs a hierarchical decision-making structure with conditional action masking, where high-level actions direct the robot's exploration, while low-level actions optimize its navigation and efficient chemical spraying in affected areas. The key objectives of optimization include improving the coverage of infected areas with limited battery power and reducing chemical usage, thus preventing unnecessary spraying of healthy areas of the field. Our numerical experimental results demonstrate that the proposed method, Hierarchical Action Masking Proximal Policy Optimization (HAM-PPO), significantly outperforms baseline practices, such as LawnMower navigation + indiscriminate spraying (Carpet Spray), in terms of yield recovery and resource efficiency. HAM-PPO consistently achieves higher yield recovery percentages and lower chemical costs across a range of infection scenarios. The framework also exhibits robustness to observation noise and generalizability under diverse environmental conditions, adapting to varying infection ranges and spatial distribution patterns.

Georg Happich, Alexander Grever, Julius Schöning

The agricultural industry is undergoing a significant transformation with the increasing adoption of autonomous technologies. Addressing complex challenges related to safety and security, components and validation procedures, and liability distribution is essential to facilitate the adoption of autonomous technologies. This paper explores the collaborative groups and initiatives undertaken to address these challenges. These groups investigate inter alia three focal topics: 1) describe the functional architecture of the operational range, 2) define the work context, i.e., the realistic scenarios that emerge in various agricultural applications, and 3) the static and dynamic detection cases that need to be detected by sensor sets. Linked by the Agricultural Operational Design Domain (Agri-ODD), use case descriptions, risk analysis, and questions of liability can be handled. By providing an overview of these collaborative initiatives, this paper aims to highlight the joint development of autonomous agricultural systems that enhance the overall efficiency of farming operations.

Xiangdong Xu, Lin Chen, Hewei Meng et al.

To explore the vibration transmission characteristics of jujube mechanical harvesting, and optimize the relationship between vibration input and dynamic response of jujube branches, the vibration characteristics simulation and layered vibration test of jujube branches were carried out. The jujube branch model was established by means of three-dimensional scanning and reverse reconstruction. The natural frequency and suitable vibration parameter range of the jujube branch model were obtained by simulation. Finally, the stratified vibration field experiment of jujube branch was carried out. The results show that there are multi-order natural frequencies of jujube branch in the range of 0–30 Hz. The typical vibration modes include the overall deformation of jujube branch, the deformation of unilateral branch and the deformation of the end of twigs. The resonance frequencies of the measuring points on different branches are mostly close, but the frequencies of the maximum peaks on different paths are different, which is often related to the branch path. The optimal working parameter combination under layered vibration is: the lower layer excitation frequency and amplitude are 5.80 Hz and 7.00 mm, the upper layer excitation frequency and amplitude are 15.60 Hz and 8.50 mm. Under this parameter combination, the acceleration of the measuring point on the fine branch is closest to the separation acceleration. Under this parameter combination, the average harvest rate is 88.74 %. The research can provide reference for the development of forest fruit vibration harvesting machinery.

Jan Vařeka, Ludmila Zavadilová, Matúš Gašparík et al.

This study evaluated test-day records of clinical mastitis (CM), somatic cell count, and nine udder conformation traits. Somatic cell count was log-transformed into somatic cell score (SCS) in 10 periods, each 30 days long and overall, for the first lactation. CM is a complex disease closely connected with somatic cell count. The optimum udder conformation traits significantly affect dairy cattle health. The CM binary trait was monitored in seven periods throughout lactation, each 50 days long, and for the whole lactation. A logistic regression model was used to estimate the risk of CM. The model included a fixed effect of herd-year-season, age at first calving, and a fixed effect of the linear type traits of the random effect of the animal. The phenotypic correlations for udder conformation traits, CM, and SCS ranged from -0.13 to 0.69 and standard errors were 0.01-0.99. The highest CM incidence and SCS were observed for the medial ligament scores 1-2: convex base of the udder. According to the logistic regression assessment, the medial ligament scores 1-2: convex base of the udder and the CM incidence to 50 days in milk reported a 3.79 times higher probability of the CM incidence at the reference level (extremely deep medial ligament) at the same stage of the lactation. CM incidence and SCS significantly decreased with decreasing udder depth. Udder depth below the hock was associated with the highest risk of CM. For udder depth and the whole lactation, the CM ODDS ratio was 1.00-2.56, CM least squares means were 0.18-0.44, and SCS least squares means were 3.20-4.10. Our study confirmed that the start of lactation is critical for the onset of CM, and somatic cell count is manifested throughout lactation. The effect of the udder conformation is then observable in somatic cell count and CM during the whole lactation.

W. Berry