Special Issue "Optimization of Resource Use for Productivity, Efficiency, and Sustainability in Agriculture"

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Economics, Policies and Rural Management".

Deadline for manuscript submissions: 20 November 2021.

Special Issue Editors

Dr. Vítor João Pereira Domingues Martinho
E-Mail Website
Guest Editor
Agricultural School (ESAV) and CERNAS-IPV Research Centre, Polytechnic Institute of Viseu (IPV), Viseu, Portugal
Interests: agricultural economics; sustainability; land use; regional planning
Special Issues and Collections in MDPI journals
Prof. Dr. Paulo Reis Mourão
E-Mail Website
Guest Editor
Department of Economics & NIPE, Economics & anagement School, University of Minho, 4700 Braga, Portugal
Interests: economics; empirical; applied economics and finance; social economics; econometric method
Special Issues and Collections in MDPI journals
Prof. Dr. Nikolaos Georgantzis
E-Mail Website
Guest Editor
Burgundy School of Business Dijon, School of Wine and Spirits Business, Dijon, France
Interests: viticulture; wine economics; decision support systems; socioeconomic determinants of innovation adoption; consumer preferences; pesticide management; environmental and economic impacts

Special Issue Information

Dear Colleagues,

A dynamic agricultural sector is crucial for any country’s economy and society. From an economic point of view, it is desirable that agricultural products can be supplied to the market under conditions which are favorable to the consumer and, at the same time, fair for the producers. The whole process is particularly challenging in the presence of increasingly strong requirements for competitiveness to be combined with the goals of sustainability, maintaining the balance between economic and environmental performance. In fact, in a context of climate change and global warming, it is necessary to reduce the sector's ecological footprint and for this, innovation and entrepreneurship can make important contributions. Technological advances are also decisive for improving the efficiency and productivity of farms, with special emphasis on the use of water, soil, and energy, without compromising food production.

In this perspective, this Special Issue aims to provide new insights about the efficient use of resources on farms for more sustainable and integrated agriculture.

Specifically, this Special Issue welcomes submissions on the following topics:

  • Development of models for efficient use of resources, based, specifically, on DEA (data envelopment analysis), SFA (stochastic frontier analysis), and linear and mixed programming approaches;
  • Identification of innovative and sustainable methodologies for farm planning;
  • New contributions on agricultural entrepreneurship;
  • New approaches to address the impacts of climate change on agriculture and reduce the sector's ecological footprint;
  • Analysis of agricultural policies on the competitiveness of farms;
  • Innovative ways to link agriculture with other economic sectors;
  • Strategies to improve the participation of farmers in agri-food chains, namely, to bring them closer to final consumers;
  • Decision support systems for agriculture;
  • Socioeconomic aspects of innovative farming processes and protocol adoption;
  • Integrated pesticide management and pesticide reduction strategies;
  • Farming cooperatives and farm networks.

Dr. Vítor João Pereira Domingues Martinho
Prof. Dr. Paulo Reis Mourão
Prof. Dr. Nikolaos Georgantzis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data envelopment analysis (DEA)
  • linear and mixed programming
  • Malmquist index
  • stochastic frontier analysis (SFA)
  • global warming
  • climate change
  • WEFS nexus
  • agricultural economics and management
  • environmental economics
  • regional economics
  • agricultural policies
  • agricultural planning
  • agricultural innovation
  • agricultural entrepreneurship
  • pesticide management
  • socioeconomic determinants of innovation adoption

Published Papers (3 papers)

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Research

Article
Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach
Agriculture 2021, 11(9), 837; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11090837 - 31 Aug 2021
Viewed by 492
Abstract
This research illustrates the technical efficiency of the pan-India paddy cultivation status obtained through a stochastic frontier approach. The results suggest that the mean technical efficiency varies from 0.64 in Gujarat to 0.95 in Odisha. Inputs like human labor, mechanical labor, fertilizer, irrigation [...] Read more.
This research illustrates the technical efficiency of the pan-India paddy cultivation status obtained through a stochastic frontier approach. The results suggest that the mean technical efficiency varies from 0.64 in Gujarat to 0.95 in Odisha. Inputs like human labor, mechanical labor, fertilizer, irrigation and insecticide were found to determine the yield in paddy cultivation across India (except for Chhattisgarh). Inefficiency in the paddy production in Punjab, Bihar, West Bengal, Andhra Pradesh, Tamil Nadu, Kerala, Assam, Gujarat and Odisha in 2016–2017 was caused by technical inefficiency due to poor input management, as suggested by the significant σ2U and σ2v values of the stochastic frontier model. In addition, most of the farm groups in the study operated in the high-efficiency group (80–90% technical efficiency). No specific pattern of input use can be visualized through descriptive measures to give any specific policy implication. Thus, machine learning algorithms based on the input parameters were tested on the data in order to predict the farmers’ efficiency class for individual states. The highest mean accuracy of 0.80 for the models of all of the states was achieved in random forest models. Among the various states of India, the best random forest prediction model based on accuracy was fitted to the input data of Bihar (0.91), followed by Uttar Pradesh (0.89), Andhra Pradesh (0.88), Assam (0.88) and West Bengal (0.86). Thus, the study provides a technique for the classification and prediction of a farmer’s efficiency group from the levels of input use in paddy cultivation for each state in the study. The study uses the DES input dataset to classify and predict the efficiency group of the farmer, as other machine learning models in agriculture have used mostly satellite, spectral imaging and soil property data to detect disease, weeds and crops. Full article
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Article
Improved Rice Technology Adoption: The Role of Spatially-Dependent Risk Preference
Agriculture 2021, 11(8), 691; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11080691 - 22 Jul 2021
Viewed by 650
Abstract
This study analyses farmers’ adoption of improved rice technology, taking into account farmers’ risk preferences; the unobserved spatial heterogeneity associated with farmers’ risk preferences; farmers’ household and farm characteristics; farm locations, farmers’ access to information, and their perceptions on the rice improved varieties [...] Read more.
This study analyses farmers’ adoption of improved rice technology, taking into account farmers’ risk preferences; the unobserved spatial heterogeneity associated with farmers’ risk preferences; farmers’ household and farm characteristics; farm locations, farmers’ access to information, and their perceptions on the rice improved varieties (i.e., high yield varieties, HYV). The study used data obtained from field experiments and a survey conducted in 2016 in Nigeria. An instrumental-variable probit model was estimated to account for potential endogenous farmers’ risk preference in the adoption decision model. Results show that risk averse (risk avoidant) farmers are less likely to adopt HYV, with the spatial lags of farmers’ risk attitudes found to be a good instrument for spatially unobserved variables (e.g., environmental and climatic factors). We conclude that studies supporting policy action aiming at the diffusion of improved rice varieties need to collect information, if possible, on farmers’ risk attitudes, local environmental and climatic conditions (e.g., climatic, topographic, soil quality, pest incidence) in order to ease the design and evaluation of policy actions on the adoption of improved agricultural technology. Full article
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Article
Fragmentation Reduction through Farmer-Led Land Transfer and Consolidation? Experiences of Rice Farmers in Wuhan Metropolitan Area, China
Agriculture 2021, 11(7), 631; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11070631 - 06 Jul 2021
Viewed by 490
Abstract
Land fragmentation has become a serious obstacle to agricultural production, and land transfer and consolidation are traditionally emphasized as the two most effective solutions to this quandary. To identify the extent of land fragmentation accurately and systematically, this study selected the number of [...] Read more.
Land fragmentation has become a serious obstacle to agricultural production, and land transfer and consolidation are traditionally emphasized as the two most effective solutions to this quandary. To identify the extent of land fragmentation accurately and systematically, this study selected the number of plots, the average size of plots, and the average distance between plots to calculate the land fragmentation index (LFI). Taking the Wuhan metropolitan area as a case study, this study examined the effectiveness of farmer-led land transfer and consolidation on land fragmentation. The main results are as follows: (a) most of the transferred plots and contracted plots were not spatially adjacent, suggesting that the tenants could not merge and consolidate both plots; (b) land transfer caused the LFI to increase by 2.85%, suggesting that land transfer had intensified the degree of land fragmentation to some extent; (c) if the transferred and contracted plots were non-adjacent or adjacent but unmerged and unconsolidated, then the LFI might increase or decrease; (d) if the transferred and contracted plots were spatially adjacent, merged, and consolidated, then the LFI decreased significantly. Full article
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