Hasil untuk "Agriculture"

K. Chomitz, D. Gray

M. Peoples, D. Herridge, J. Ladha

Esperanza Huerta Lwanga, N. Beriot, F. Corradini et al.

This paper explores different interactions and processes involved in the transport of microplastics from agricultural systems to surrounding environments. We conducted an exhaustive review of the most recent scientific papers on microplastic transport in terrestrial systems, with an emphasis on agricultural systems. In the following sections, several aspects of this problem are discussed, namely (i) direct and indirect sources of microplastics, (ii) biotic and abiotic transportation of microplastics in and from the terrestrial environment, (iii) modelling of microplastics in the terrestrial environment and (iv) facilitated chemicals and pathogens in combination with plastic particles. There is very little information available concerning microplastic transport in the terrestrial environment; therefore, more research is needed to gain a better understanding of how these processes take place. The novelty of this review lies in assessing how microplastic transport occurs from the plastisphere (cellular) to the landscape level and from agricultural systems to the surrounding areas. Graphical Abstract

J. Dupouey, E. Dambrine, Jean-Denis Laffite et al.

J. Böhlke

F. Caselli, Wilbur John Coleman II

O. Fasusi, C. Cruz, O. Babalola

The world’s human population continues to increase, posing a significant challenge in ensuring food security, as soil nutrients and fertility are limited and decreasing with time. Thus, there is a need to increase agricultural productivity to meet the food demands of the growing population. A high level of dependence on chemical fertilizers as a means of increasing food production has damaged the ecological balance and human health and is becoming too expensive for many farmers to afford. The exploitation of beneficial soil microorganisms as a substitute for chemical fertilizers in the production of food is one potential solution to this conundrum. Microorganisms, such as plant growth-promoting rhizobacteria and mycorrhizal fungi, have demonstrated their ability in the formulation of biofertilizers in the agricultural sector, providing plants with nutrients required to enhance their growth, increase yield, manage abiotic and biotic stress, and prevent phytopathogens attack. Recently, beneficial soil microbes have been reported to produce some volatile organic compounds, which are beneficial to plants, and the amendment of these microbes with locally available organic materials and nanoparticles is currently used to formulate biofertilizers to increase plant productivity. This review focuses on the important role performed by beneficial soil microorganisms as a cost-effective, nontoxic, and eco-friendly approach in the management of the rhizosphere to promote plant growth and yield.

D. Rigby, D. Cáceres

P. Krasilnikov, M. Taboada, Amanullah

Due to the growing population and consequent pressure of use, agricultural soils must maintain adequate levels of quantity and quality to produce food, fiber, and energy, without falling victim to a negative impact on their balance of nutrients, health, or their ability to function [...]

Yifang Liu, Dongshen Sun, Huijuan Wang et al.

Abstract Green development and low-carbon economy play important roles to achieve sustainable society. Since agricultural production is the foundation of the Chinese national economy, agricultural green production acts as the driving force in green economy development, as well as a prerequisite to realize green behavior and sustainable ecology. Different from traditional agricultural production, the idea of agricultural green production also put many factors including economy, environment, and social development into comprehensive consideration. It provides the future directions of China’s agriculture. This research aims to systematically sort the agricultural green production goals from five dimensions: supply capacity, resource utilization, environment quality, ecosystem maintenance, and farmers’ lives. An agricultural green production assessment index system was constructed based on national agricultural census data indicators. Furthermore, the gap between China’s agricultural green production status and target value, as well as the vertical and spatial evolution of agricultural green production levels in China have been determined empirically relying on the data from three national agricultural censuses and additional statistical data from national statistical yearbooks and coefficient manuals. Moreover, key recommendations were provided for path optimizing and China’s agricultural green production upgrading.

D. Pimentel, B. Berger, David Filiberto et al.

T. Benton, D. Bryant, L. Cole et al.

J. Paavola

Dimitrios Chatziparaschis, Elia Scudiero, Brent Sams et al.

The dynamic and heterogeneous nature of agricultural fields presents significant challenges for object detection and localization, particularly for autonomous mobile robots that are tasked with surveying previously unseen unstructured environments. Concurrently, there is a growing need for real-time detection systems that do not depend on large-scale manually labeled real-world datasets. In this work, we introduce a comprehensive annotation-to-detection framework designed to train a robust multi-modal detector using limited and partially labeled training data. The proposed methodology incorporates cross-modal annotation transfer and an early-stage sensor fusion pipeline, which, in conjunction with a multi-stage detection architecture, effectively trains and enhances the system's multi-modal detection capabilities. The effectiveness of the framework was demonstrated through vine trunk detection in novel vineyard settings that featured diverse lighting conditions and varying crop densities to validate performance. When integrated with a customized multi-modal LiDAR and Odometry Mapping (LOAM) algorithm and a tree association module, the system demonstrated high-performance trunk localization, successfully identifying over 70% of trees in a single traversal with a mean distance error of less than 0.37m. The results reveal that by leveraging multi-modal, incremental-stage annotation and training, the proposed framework achieves robust detection performance regardless of limited starting annotations, showcasing its potential for real-world and near-ground agricultural applications.

Heba Shakeel, Tanvir Ahmad, Tanya Liyaqat et al.

As the volume of unstructured text continues to grow across domains, there is an urgent need for scalable methods that enable interpretable organization, summarization, and retrieval of information. This work presents a unified framework for interpretable topic modeling, zero-shot topic labeling, and topic-guided semantic retrieval over large agricultural text corpora. Leveraging BERTopic, we extract semantically coherent topics. Each topic is converted into a structured prompt, enabling a language model to generate meaningful topic labels and summaries in a zero-shot manner. Querying and document exploration are supported via dense embeddings and vector search, while a dedicated evaluation module assesses topical coherence and bias. This framework supports scalable and interpretable information access in specialized domains where labeled data is limited.

Shun Hattori, Hikaru Sasaki, Takumi Hachimine et al.

Vision-based imitation learning has shown promise for robotic manipulation; however, its generalization remains limited in practical agricultural tasks. This limitation stems from scarce demonstration data and substantial visual domain gaps caused by i) crop-specific appearance diversity and ii) background variations. To address this limitation, we propose Dual-Region Augmentation for Imitation Learning (DRAIL), a region-aware augmentation framework designed for generalizable vision-based imitation learning in agricultural manipulation. DRAIL explicitly separates visual observations into task-relevant and task-irrelevant regions. The task-relevant region is augmented in a domain-knowledge-driven manner to preserve essential visual characteristics, while the task-irrelevant region is aggressively randomized to suppress spurious background correlations. By jointly handling both sources of visual variation, DRAIL promotes learning policies that rely on task-essential features rather than incidental visual cues. We evaluate DRAIL on diffusion policy-based visuomotor controllers through robot experiments on artificial vegetable harvesting and real lettuce defective leaf picking preparation tasks. The results show consistent improvements in success rates under unseen visual conditions compared to baseline methods. Further attention analysis and representation generalization metrics indicate that the learned policies rely more on task-essential visual features, resulting in enhanced robustness and generalization.

A. Bouwman, A. Beusen, G. Billen

J. Pongratz, C. Reick, T. Raddatz et al.

Lili Guo, Shuang Zhao, Yuting Song et al.

This study aimed to understand green finance’s impact on fertilizer use and agricultural carbon emissions. We selected the macro panel data of 30 provinces (cities) in China from 2000 to 2019. The main research methods are standardized test framework (cross-sectional dependence, unit root and cointegration test), the latest causal test, impulse response, and variance decomposition analysis. Examined the long-term equilibrium relationship between green finance, fertilizer use, and agricultural carbon emissions. The results show: fertilizer consumption and agricultural carbon emissions have a positive correlation. However, green finance can significantly reduce agricultural carbon emissions. The causal test confirmed the bidirectional causal relationship between agricultural carbon emissions and fertilizer use. At the same time, verified one-way causality from green finance to both of them. Interpret the results of impulse response and variance decomposition analysis: among the changes in agricultural carbon emissions, chemical fertilizers contributed 2.45%, green finance contributed 4.34%. In addition, the contribution rate of green finance to chemical fertilizer changes reached 11.37%. Green finance will make a huge contribution to reducing fertilizer use and agricultural carbon emissions within a decade. The research conclusions provide an important scientific basis for China’s provinces (cities) to formulate carbon emission reduction policies. China has initially formed a policy system and market environment to support the development of green finance, in 2020, the “dual carbon” goal was formally proposed. In 2021, the national “14th Five-Year Plan” and the 2035 Vision Goals emphasized the importance of green finance. It plays an important supporting role in carbon emission reduction goals, and green finance has become an important pillar of national strategic goals.

Q. Jiang, Jizhi Li, Hongyun Si et al.

Whether the digital economy can effectively promote agricultural green development is crucial to the realization of agricultural rural modernization. This study empirically analyzes the impact of the digital economy on agricultural green development and the mechanism of action based on panel data of 30 Chinese provinces from 2011 to 2020. The results reveal that (1) the digital economy can significantly improve the green development level of China’s agriculture; the dividends in the eastern region and central region are significantly higher than that in the western region, and there is regional heterogeneity. (2) The role of the digital economy in promoting agricultural green development has a nonlinear characteristic of increasing “marginal effect.” (3) The digital economy has a significant spatial spillover effect, which can have a positive impact on agricultural green development in the surrounding areas. (4) The construction of “Broadband Countryside” can improve the development of the rural digital economy and indirectly promote agricultural green development. This study deepens our understanding of the internal effect and interval relationship of how the digital economy enables agricultural green development and provides the theoretical basis and practical suggestions for optimizing digital facility construction and high-quality agricultural development.