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Sustainability of Farming in Future: Developing of Sustainable Intensification

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6967

Special Issue Editor


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Guest Editor
Centre for Rural Economy, School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK
Interests: efficiency and productivity analysis; sustainability; spatial econometrics; choice modelling

Special Issue Information

Dear Colleagues,

The concept of sustainable intensification (SI) has been adopted by policy makers as a way to protecting biodiversity and the provision pf ecosystem services while meeting a growing demand for food in the world. However, SI is a widely debated concept that presents overlapping aspects between its various definitions (e.g. achieving higher yields whilst protecting the environment) but also with important differences. Whereas some SI definitions explicitly state that SI cannot be achieved by expanding agricultural land (Pretty, 1997; Royal Society, 2009), other definitions leave this open or are more flexible on this “constrain” (Foresight report, 2011). The fact that there is no unique interpretation/definition of SI metrics has led to the development of a variety of approaches and methods to produce metrics of SI (Franks, 2014; Areal et al. 2018).

We are interested in papers that develop novel SI indicators at local, regional, or national level; papers that provide insights into farmer attitudes towards SI, the role of the supply chain in influencing farmer decisions towards SI; papers that incorporate spatial variation regarding environmental impacts and risks associated with food production capability (for food production and opportunities for other ecosystem services) and environmental risks; papers that discuss as well as investigate the role of communication and knowledge exchange of innovations on achieving SI.

Further suggestions within the area of developing of Sustainable Intensification are welcome.

Dr. Francisco J. Areal
Guest Editor

Manuscript Submission Information

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Keywords

  • Environmental impact indicators
  • Social impact indicators
  • Metrics, indicators of sustainable intensification
  • Methods to measure sustainable intensification
  • Future farming
  • Spatial aspects of sustainable intensification
  • Farm efficiency
  • Competitiveness
  • Food security
  • Animal welfare
  • Crop and livestock farming

Published Papers (3 papers)

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Research

13 pages, 2036 KiB  
Article
Measuring Sustainable Intensification Using Satellite Remote Sensing Data
by Francisco J. Areal, Wantao Yu, Kevin Tansey and Jiahuan Liu
Sustainability 2022, 14(3), 1832; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031832 - 05 Feb 2022
Cited by 1 | Viewed by 1901
Abstract
Farm-level sustainable intensification metrics are needed to evaluate farm performance and support policy-making processes aimed at enhancing sustainable production. Farm-level sustainable intensification metrics require environmental impacts associated with agricultural production to be accounted for. However, it is common that such indicators are not [...] Read more.
Farm-level sustainable intensification metrics are needed to evaluate farm performance and support policy-making processes aimed at enhancing sustainable production. Farm-level sustainable intensification metrics require environmental impacts associated with agricultural production to be accounted for. However, it is common that such indicators are not available. We show how satellite-based remote sensing information can be used in combination with farm efficiency analysis to obtain a sustainable intensification (SI) indicator, which can serve as a sustainability benchmarking tool for farmers and policy makers. We obtained an SI indicator for 114 maize farms in Yangxin County, located in the Shandong Province in China, by combining information on maize output and inputs with satellite information on the leaf area index (from which a nitrogen environmental damage indicator is derived) into a farm technical efficiency analysis using a stochastic frontier approach. We compare farm-level efficiency scores between models that incorporate environmental damage indicators based on satellite-based remote sensing information and models that do not account for environmental impact. The results demonstrate that (a) satellite-based information can be used to account for environmental impacts associated with agriculture production and (b) how the environmental impact metrics derived from satellite-based information combined with farm efficiency analysis can be used to obtain a farm-level sustainable intensification indicator. The approach can be used to obtain tools for farmers and policy makers aiming at improving SI. Full article
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16 pages, 595 KiB  
Article
Highlighting the Drivers of Italian Diversified Farms Efficiency: A Two-Stage DEA-Panel Tobit Analysis
by Luca Romagnoli, Vincenzo Giaccio, Luigi Mastronardi and Maria Bonaventura Forleo
Sustainability 2021, 13(23), 12949; https://0-doi-org.brum.beds.ac.uk/10.3390/su132312949 - 23 Nov 2021
Cited by 3 | Viewed by 1522
Abstract
Farm diversification is an important phenomenon in agricultural systems and rural development in Europe, pursuing economic, social and environmental goals. For the sustainability of diversified farms, it is important to analyse some drivers affecting farm efficiency, for instance, socio-economic, technical and policy drivers. [...] Read more.
Farm diversification is an important phenomenon in agricultural systems and rural development in Europe, pursuing economic, social and environmental goals. For the sustainability of diversified farms, it is important to analyse some drivers affecting farm efficiency, for instance, socio-economic, technical and policy drivers. The efficiency performance of a panel of Italian farms practising other gainful activities in the period 2012–2017 was investigated and regressed against the drivers that mostly affects farm performances. FADN data and a two-step approach were used. An output-oriented Data Envelopment Analysis was applied; in the second step, efficiency scores were used as a dependent variable in a panel Tobit regression analysis used to determine differences in the significance of drivers. Social, economic, technical and policy drivers were considered as explanatory variables. Results show margins for improving farms performances. The incidence of the output from other gainful activities has been proven to positively affect farms efficiencies, while intermediate costs are the most negatively impacting factor. As regards policy variables and implications, the significance of localization in mountain disadvantaged territories further supports the relevance of EU subsidies in less-favoured areas. Managerial implications in terms of technical, structural and economic indicators can be drawn from study findings. Full article
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16 pages, 1627 KiB  
Article
Human Impact Promotes Sustainable Corn Production in Hungary
by Tibor András Marton, Anna Kis, Anna Zubor-Nemes, Anikó Kern and Nándor Fodor
Sustainability 2020, 12(17), 6784; https://0-doi-org.brum.beds.ac.uk/10.3390/su12176784 - 21 Aug 2020
Cited by 5 | Viewed by 2551
Abstract
We aim to predict Hungarian corn yields for the period of 2020–2100. The purpose of the study was to mutually consider the environmental impact of climate change and the potential human impact indicators towards sustaining corn yield development in the future. Panel data [...] Read more.
We aim to predict Hungarian corn yields for the period of 2020–2100. The purpose of the study was to mutually consider the environmental impact of climate change and the potential human impact indicators towards sustaining corn yield development in the future. Panel data regression methods were elaborated on historic observations (1970–2018) to impose statistical inferences with simulated weather events (2020–2100) and to consider developing human impact for sustainable intensification. The within-between random effect model was performed with three generic specifications to address time constant indicators as well. Our analysis on a gridded Hungarian database confirms that rising temperature and decreasing precipitation will negatively affect corn yields unless human impact dissolves the climate-induced challenges. We addressed the effect of elevated carbon dioxide (CO2) as an important factor of diverse human impact. By superposing the human impact on the projected future yields, we confirm that the negative prospects of climate change can be defeated. Full article
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