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Economics of Climate Smart Agriculture
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Economics of Climate Smart Agriculture

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 19151

Special Issue Editor

Prof. Dr. Sanzidur Rahman

E-Mail Website1 Website2
Guest Editor
School of Agriculture, Policy and Development (SAPD), University of Reading, Reading RG6 6UR, UK
Interests: agricultural economics; productivity and efficiency; technological progress in agriculture; sustainable agriculture; sustainable livelihoods; poverty and nutrition; international development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate Smart Agriculture (CSA) is an approach aimed at transforming and reorienting existing agricultural systems to support food security and development in a sustainable manner under a changing climate. CSA refers to agriculture that sustainably increases productivity, enhances adaptation and resilience, reduces GHGs or mitigate where possible, and enhances achievement of national food security and development. CSA helps to address a number of important challenges in developing countries, e.g., food security, malnutrition, poverty and interdependent relationship between climate change and agriculture. Nevertheless, widespread and upscaling of CSA will be possible if the approach is economically and socially viable

This Special Issue aimed at soliciting original contributions from academics, researchers, practitioners, NGOs and other stakeholders providing theoretical insights and/or empirical analysis focusing on economic and social viability of CSA that can provide valuable lessons for the future. The editor encourages submissions applying cross-disciplinary approaches and use of a variety of quantitative, qualitative and mixed methodologies in social sciences. The scope of submission includes original research and review articles that address the issues raised above.

Dr. Sanzidur Rahman
Guest Editor

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 submissions that pass pre-check are 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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • Climate Smart Agriculture (CSA)
  • CSA technologies
  • Adoption of CSA technologies
  • Economics of CSA
  • Sustainability of CSA
  • CSA and food security
  • CSA and poverty/inequality
  • Gender roles in CSA
  • CSA and the rural economy

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Published Papers (3 papers)

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Research

12 pages, 3453 KiB  
Article
Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data
by Juan Rincon-Patino, Emmanuel Lasso and Juan Carlos Corrales
Sustainability 2018, 10(10), 3498; https://doi.org/10.3390/su10103498 - 29 Sep 2018
Cited by 11 | Viewed by 8065
Abstract
Persea americana, commonly known as avocado, is becoming increasingly important in global agriculture. There are dozens of avocado varieties, but more than 85% of the avocados harvested and sold in the world are of the Hass one. Furthermore, information on the market [...] Read more.
Persea americana, commonly known as avocado, is becoming increasingly important in global agriculture. There are dozens of avocado varieties, but more than 85% of the avocados harvested and sold in the world are of the Hass one. Furthermore, information on the market of agricultural products is valuable for decision-making; this has made researchers try to determine the behavior of the avocado market, based on data that might affect it one way or another. In this paper, a machine learning approach for estimating the number of units sold monthly and the total sales of Hass avocados in several cities in the United States, using weather data and historical sales records, is presented. For that purpose, four algorithms were evaluated: Linear Regression, Multilayer Perceptron, Support Vector Machine for Regression and Multivariate Regression Prediction Model. The last two showed the best accuracy, with a correlation coefficient of 0.995 and 0.996, and a Relative Absolute Error of 7.971 and 7.812, respectively. Using the Multivariate Regression Prediction Model, an application that allows avocado producers and sellers to plan sales through the estimation of the profits in dollars and the number of avocados that could be sold in the United States was created. Full article
(This article belongs to the Special Issue Economics of Climate Smart Agriculture)
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17 pages, 1356 KiB  
Article
Environment-Smart Agriculture and Mapping of Interactions among Environmental Factors at the Farm Level: A Directed Graph Approach
by Noor-E-Sabiha and Sanzidur Rahman
Sustainability 2018, 10(5), 1580; https://doi.org/10.3390/su10051580 - 15 May 2018
Cited by 10 | Viewed by 5085
Abstract
Environment-smart agriculture (ESA) aims at sustaining increased agricultural production while limiting negative impacts on the environment. The present study develops an index of composite on-farm environmental impacts (COEI) as a proxy measure to evaluate ESA and validates the index by mapping interactions amongst [...] Read more.
Environment-smart agriculture (ESA) aims at sustaining increased agricultural production while limiting negative impacts on the environment. The present study develops an index of composite on-farm environmental impacts (COEI) as a proxy measure to evaluate ESA and validates the index by mapping interactions amongst agriculture related environmental impacts and potential constraints to practice ESA by using the directed graph approach. The cost of mitigation to practice ESA was calculated by estimating the cost of reducing on-farm environmental impacts by using the damage–cost method. The approach was empirically applied to a sample of 317 High Yielding Variety (HYV) rice farms from three intensive rice-growing regions of northwestern Bangladesh. Results showed that the use of chemical pesticides contributed towards higher level of uncertainty in practicing ESA than the use of chemical fertilizers, irrigation and household pollution. The combined effect of the influence from these factor interactions was estimated at 2.3, which falls in the critical region of influence and implies extreme level of uncertainty in practicing ESA. The cost of mitigating negative environmental impacts is higher for the problems of ‘decline in soil fertility’, ‘increases in crop diseases’ and ‘reduction in fish catch’ as compared to other soil and water related impacts. Policy implications include investments in addressing the problems of ‘soil fertility decline’, ‘increases in crop diseases’ and ‘reduction in fish catch’ and raising farmers’ awareness on using farm chemicals to promote ESA practices for HYV rice production. Full article
(This article belongs to the Special Issue Economics of Climate Smart Agriculture)
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18 pages, 1456 KiB  
Article
A Model Approach for Yield-Zone-Specific Cost Estimation of Greenhouse Gas Mitigation by Nitrogen Fertilizer Reduction
by Yusuf Nadi Karatay and Andreas Meyer-Aurich
Sustainability 2018, 10(3), 710; https://doi.org/10.3390/su10030710 - 6 Mar 2018
Cited by 10 | Viewed by 4698
Abstract
Nitrogen use in agriculture has been intensified to feed the growing world population, which led to concerns on environmental harms, including greenhouse gas emissions. A reduction in nitrogen fertilization can abate greenhouse gas emissions, however, it may result in crop yield penalties and, [...] Read more.
Nitrogen use in agriculture has been intensified to feed the growing world population, which led to concerns on environmental harms, including greenhouse gas emissions. A reduction in nitrogen fertilization can abate greenhouse gas emissions, however, it may result in crop yield penalties and, accordingly, income loss. Assessment tools are necessary to understand the dynamics of nitrogen management issues both in environmental and economic aspects and both at low and high aggregation levels. Our study presents a model approach, estimating yield-zone-specific costs of greenhouse gas mitigation by moderate reduction of mineral nitrogen fertilizer application. Comparative advantages of mitigating greenhouse gas emissions by nitrogen fertilizer reduction were simulated for five yield-zones with different soil fertility in the state of Brandenburg, Germany. The results suggest that differences in yield response to nitrogen fertilizer lead to considerable differences in greenhouse gas mitigation costs. Overall cost-efficiency of a regional greenhouse gas mitigation by nitrogen fertilizer reduction can be substantially improved, if crop and yield-zone-specific yield responses are taken into account. The output of this study shall help to design cost-efficient agro-environmental policies targeting with specific crop yield response functions at different sites. Full article
(This article belongs to the Special Issue Economics of Climate Smart Agriculture)
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