Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
162,913
result(s) for
"Agricultural management"
Sort by:
Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture
Introducing machine vision-based automation to the agricultural sector is essential to meet the food demand of a rapidly growing population. Furthermore, extensive labor and time are required in agriculture; hence, agriculture automation is a major concern and an emerging subject. Machine vision-based automation can improve productivity and quality by reducing errors and adding flexibility to the work process. Primarily, machine vision technology has been used to develop crop production systems by detecting diseases more efficiently. This review provides a comprehensive overview of machine vision applications for stress/disease detection on crops, leaves, fruits, and vegetables with an exploration of new technology trends as well as the future expectation in precision agriculture. In conclusion, research on the advanced machine vision system is expected to develop the overall agricultural management system and provide rich recommendations and insights into decision-making for farmers.
Journal Article
A Review on Evapotranspiration Estimation in Agricultural Water Management: Past, Present, and Future
Evapotranspiration (ET) is a major component of the water cycle and agricultural water balance. Estimation of water consumption over agricultural areas is important for agricultural water resources planning, management, and regulation. It leads to the establishment of a sustainable water balance, mitigates the impacts of water scarcity, as well as prevents the overusing and wasting of precious water resources. As evapotranspiration is a major consumptive use of irrigation water and rainwater on agricultural lands, improvements of water use efficiency and sustainable water management in agriculture must be based on the accurate estimation of ET. Applications of precision and digital agricultural technologies, the integration of advanced techniques including remote sensing and satellite technology, and usage of machine learning algorithms will be an advantage to enhance the accuracy of the ET estimation in agricultural water management. This paper reviews and summarizes the technical development of the available methodologies and explores the advanced techniques in the estimation of ET in agricultural water management and highlights the potential improvements to enhance the accuracy of the ET estimation to achieve precise agricultural water management.
Journal Article
Asian agribusiness management : case studies in growth, marketing, and upgrading strategies
\"This book of case studies is designed to provide useful information for instructional purposes and for those interested in the management of Asian agribusiness. This collected volume of case studies is organized around three major themes--growth, marketing, and upgrading strategies. Many of the cases herein were used in Advanced Agribusiness Workshops jointly organized by the Asian Productivity Organization and Cornell University held in Bangkok, Manila, and Bali. Through a case study-driven approach, this book offers an opportunity for students, policymakers, and business owners to consider the impact of key trends like value-addition, urbanization, the environment, regional integration, climate change, and technology on Asian agribusinesses\"-- Provided by publisher.
Book
AI-driven optimization of agricultural water management for enhanced sustainability
Optimizing agricultural water resource management is crucial for food production, as effective water management can significantly improve irrigation efficiency and crop yields. Currently, precise agricultural water demand forecasting and management have become key research focuses; however, existing methods often fail to capture complex spatial and temporal dependencies. To address this, we propose a novel deep learning framework that combines remote sensing technology with the UNet-ConvLSTM (UCL) model to effectively integrate spatial and temporal features from MODIS and GLDAS datasets. Our model leverages the high-resolution spatial data from UNet and the temporal dependencies captured by ConvLSTM to significantly improve prediction accuracy. Experimental results demonstrate that our UCL model achieves the best
R
2
compared to existing methods, reaching 0.927 on the MODIS dataset and 0.935 on the GLDAS dataset. This approach highlights the potential of AI and remote sensing technologies in addressing critical challenges in agricultural water management, contributing to more sustainable and efficient food production systems.
Journal Article
Integrated drought management
\"The first volume of this comprehensive global perspective on Integrated Drought Management is focused on understanding drought, causes, and the assessment of drought impacts. It explains different types of drought: agricultural, meteorological, hydrological, and socio-economic droughts, their indices and the impact of climate change on drought. The volume also examines spatio-temporal analysis of drought, variability and patterns, assessment, and drought evaluation. With numerous case studies from India, Mexico, Turkey, Brazil, US, and other countries, this volume serves as a valuable resource for all readers who want to advance their knowledge on drought and risk management\"-- Provided by publisher.
BOOK
Tillage shapes the soil and rhizosphere microbiome of barley—but not its susceptibility towards Blumeria graminis f. sp. hordei
ABSTRACT
Long-term agricultural practices are assumed to shape the rhizosphere microbiome of crops with implications for plant health. In a long-term field experiment, we investigated the effect of different tillage and fertilization practices on soil and barley rhizosphere microbial communities by means of amplicon sequencing of 16S rRNA gene fragments from total community DNA. Differences in the microbial community composition depending on the tillage practice, but not the fertilization intensity were revealed. To examine whether these soil and rhizosphere microbiome differences influence the plant defense response, barley (cultivar Golden Promise) was grown in field or standard potting soil under greenhouse conditions and challenged with Blumeria graminis f. sp. hordei (Bgh). Amplicon sequence analysis showed that preceding tillage practice, but also aboveground Bgh challenge significantly influenced the microbial community composition. Expression of plant defense-related genes PR1b and PR17b was higher in challenged compared to unchallenged plants. The Bgh infection rates were strikingly lower for barley grown in field soil compared to potting soil. Although previous agricultural management shaped the rhizosphere microbiome, no differences in plant health were observed. We propose therefore that the management-independent higher microbial diversity of field soils compared to potting soils contributed to the low infection rates of barley.
Tillage shaped the soil and rhizosphere microbiota of barley and its high microbial diversity prevented infection with Blumeria graminis f. sp. hordei in contrast to potting soil with low diversity.
Journal Article
The Impact of New Agricultural Management Entities’ Participation on the Transfer Price of Contracted Land Management Rights: Evidence from Northeast China
The significant transformation of agricultural production and operation models has reshaped the supply-demand structure of rural land, providing growth opportunities for new agricultural management entities characterized by large-scale operation. Their large-scale land demand has directly driven an upward trend in the transfer prices of contracted land management rights. By analyzing this practical phenomenon, this study explores the intrinsic logic behind the rising transfer prices of contracted land management rights under the participation of new agricultural management entities, aiming to provide references for further regulating the formation mechanism of transfer prices and promoting the healthy development of the land transfer market. Based on the sample survey data of farmers from the Songnen Plain and Sanjiang Plain in Northeast China, this study adopts the cluster-robust Ordinary Least Squares (OLS) model and moderating effect model for analysis. The results show that the participation of new agricultural management entities exerts a positive impact on the transfer price of contracted land management rights; the impact of new agricultural management entities’ participation on the transfer price is positively moderated by agricultural production efficiency; and the impact also presents heterogeneity across different villages and land parcels. Compared with remote villages and paddy parcels, the participation of new agricultural management entities has a more significant impact on the transfer price of contracted land management rights in township-adjacent villages and dryland parcels. Therefore, to reasonably standardize the transfer price of contracted land management rights, efforts should focus on further strengthening policy guidance to standardize the participation mechanism of new agricultural management entities, regulating the transfer market to establish a dynamic monitoring mechanism for transfer prices, and strengthening the training and guidance for new agricultural management entities to connect and drive farmers so as to improve the agricultural production efficiency of individual farmers.
Journal Article