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Tirgariseraji M, Persson T, Jafari Y, Sabouhi Sabouni M, Mirzabaev A, Nikouei A, Shahnoushi Foroushani N. Aiming at low nitrogen leaching diets based on nitrogen fertilizer regulatory policy: A regional bio-economic assessment of the Zayandeh-Rud river basin-Iran. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119650. [PMID: 38042086 DOI: 10.1016/j.jenvman.2023.119650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 09/23/2023] [Accepted: 11/16/2023] [Indexed: 12/04/2023]
Abstract
Negative environmental impacts of nitrogen (N) intensive diets have triggered global debates on sustainable nitrogen management. Solutions such as dietary transitions, cropland reallocation and N Regulatory Policy (NRP) have been proposed to mitigate the adverse environmental impacts of N use in food production. However, there is still insufficient understanding of how NRPs could be designed to minimize negative environmental impact across diverse agro-ecological zones without sacrificing human dietary requirements. To increase this understanding, we evaluated the consequences of three NRP scenarios (low, moderate, and high N fertilizer rates) on the amount of livestock and non-livestock diet components as well as the associated N leaching and farmers' Gross Margin (GM) by optimizing the allocation of cropland between food and feed crops. We developed a bio-economic Interval Fuzzy Multi-Objective Programming (bio-economic IFMOP) model for the Zayandeh-Rud river basin, Iran, and a procedure that accounts for annual average availability of calories per capita, calorie sources from livestock and non-livestock components of three dietary preferences, and inequality in calorie distribution. The interaction among soil, climate and weather variability and NRPs across nine sub-regions of the case study region was handled by crop yield simulation using the DSSAT software. The solution of farmers' GM, derived from the optimization problem across possibilities of water fluctuations, was assessed to determine the uncertainty in GM. We also introduced an N leaching per Block of Distributed Calories (BDC) criterion based on solutions of supplied calories and associated N leaching. The upper bound of the moderate NRP scenario resulted in the smallest N leaching per BDC. This corresponded to ∼0.34, ∼0.34, ∼3.77 and 19.00 million BDC of meat, dairy, wheat and potato, respectively. Also, the upper bound of this scenario satisfied the lowest instability in farmers' GM against water fluctuation compared with low and high NRP scenarios. The affordable volume of N leaching per BDC varied across sub-regions between [1.53,3.49], [1.52,3.33], [0.76,0.99] and [0.05,0.08] kg for meat, dairy, wheat and potato, respectively. Our results highlighted both optimistic and pessimistic prospects of producing low N leaching diets. The approach of this study could also be applied to other regions and countries.
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Affiliation(s)
| | - Tomas Persson
- Division of Food Production and Society, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway.
| | | | | | - Alisher Mirzabaev
- International Rice Research Institute (IRRI), Los Baños, Philippines.
| | - Alireza Nikouei
- Social and Extension Research Department, Isfahan Agricultural and Natural Resources Research and Education Center (AREEO), Isfahan, Iran.
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Ecological–Economic Modelling of Traditional Agroforestry to Promote Farmland Biodiversity with Cost-Effective Payments. SUSTAINABILITY 2022. [DOI: 10.3390/su14095615] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Orchard meadows, a traditional agroforestry system in Switzerland combining the dual use fruit and fodder production, are declining, even though the farmland managed under agri-environmental schemes (AES) has been expanding. Despite increasing interest in agroforestry research for developing sustainable agriculture, it is poorly understood how subsidies contribute to the maintenance of trees on agricultural land and the promotion of farmland biodiversity. Therefore, the objective of the present study is to examine the effects of incentive-based AES on both farmers’ decisions regarding trees and biodiversity by developing an ecological–economic assessment model. To explore cost-effective AES, we explicitly consider the heterogeneity of farm types. We apply this integrated model to the farms in Schwarzbubenland, a small hilly region in Northern Switzerland. Results show that the adoption of AES and the compliance costs of participating in AES considerably vary among farm types, and the current AES do not provide farmers with sufficient payments to maintain any type of orchard meadows, despite the ecological benefits of orchard meadows. The integrating modeling developed in this study enables us to better understand the relationship between subsidies and biodiversity through farmers’ decisions on land use and facilitates the design of cost-effective payments for the maintenance of agroforestry.
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Kipling R, Topp C, Bannink A, Bartley D, Blanco-Penedo I, Cortignani R, del Prado A, Dono G, Faverdin P, Graux AI, Hutchings N, Lauwers L, Özkan Gülzari Ş, Reidsma P, Rolinski S, Ruiz-Ramos M, Sandars D, Sándor R, Schönhart M, Seddaiu G, van Middelkoop J, Shrestha S, Weindl I, Eory V. To what extent is climate change adaptation a novel challenge for agricultural modellers? ENVIRONMENTAL MODELLING & SOFTWARE : WITH ENVIRONMENT DATA NEWS 2019; 120:104492. [PMID: 31787839 PMCID: PMC6876672 DOI: 10.1016/j.envsoft.2019.104492] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 06/10/2019] [Accepted: 07/25/2019] [Indexed: 06/10/2023]
Abstract
Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers' views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.
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Affiliation(s)
- R.P. Kipling
- Aberystwyth University, Plas Gogerddan, Aberystwyth, Ceredigion, SY23 3EE, UK
| | | | - A. Bannink
- Wageningen Livestock Research, Wageningen University & Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
| | - D.J. Bartley
- Disease Control, Moredun Research Institute, Pentlands Science Park, Bush Loan, Penicuik, EH26 0PZ, UK
| | - I. Blanco-Penedo
- Swedish University of Agricultural Sciences, Department of Clinical Sciences, SE-750 07, Uppsala, Sweden
- IRTA, Animal Welfare Subprogram, ES-17121, Monells, Girona, Spain
| | - R. Cortignani
- Department of Agricultural and Forestry scieNcEs (DAFNE), Tuscia University, Viterbo, Italy
| | - A. del Prado
- Basque Centre for Climate Change (BC3), Edificio Sede Nº 1, Planta 1, Parque Científico de UPV/EHU, Barrio Sarriena s/n, 48940, Leioa, Bizkaia, Spain
| | - G. Dono
- Department of Agricultural and Forestry scieNcEs (DAFNE), Tuscia University, Viterbo, Italy
| | - P. Faverdin
- PEGASE, Agrocampus Ouest, INRA, Saint-Gilles, 35590, France
| | - A.-I. Graux
- PEGASE, Agrocampus Ouest, INRA, Saint-Gilles, 35590, France
| | - N.J. Hutchings
- Department of Agroecology, Aarhus University, Postbox 50, Tjele, 8830, Denmark
| | - L. Lauwers
- Flanders Research Institute for Agriculture, Fisheries and Food, Merelbeke, Belgium
- Department of Agricultural Economics, Ghent University, Ghent, Belgium
| | - Ş. Özkan Gülzari
- Wageningen Livestock Research, Wageningen University & Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
- Department of Animal and Aquacultural Sciences, Faculty of Veterinary Medicine and Biosciences, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway
- Norwegian Institute of Bioeconomy Research, P.O. Box 115, 1431 Ås, Norway
| | - P. Reidsma
- Plant Production Systems, Wageningen University & Research, P.O. Box 430, Wageningen, 6700 AK, the Netherlands
| | - S. Rolinski
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Telegraphenberg A31, D-14473, Potsdam, Germany
| | - M. Ruiz-Ramos
- Universidad Politécnica de Madrid, CEIGRAM-ETSIAAB, 28040, Madrid, Spain
| | - D.L. Sandars
- School of Water, Energy, and Environment (SWEE), Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK
| | - R. Sándor
- Agricultural Institute, Centre for Agricultural Research, Hungarian Academy of Sciences, Brunszvik u 2, Martonvásár, H-2462, Hungary
| | - M. Schönhart
- Institute for Sustainable Economic Development, BOKU University of Natural Resources and Life Sciences, Feistmantelstraße 4, 1180, Vienna, Austria
| | - G. Seddaiu
- Desertification Research Centre and Dept. Agricultural Sciences, Univ. Sassari, Sassari, Italy
| | - J. van Middelkoop
- Wageningen Livestock Research, Wageningen University & Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
| | | | - I. Weindl
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Telegraphenberg A31, D-14473, Potsdam, Germany
- Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469, Potsdam, Germany
| | - V. Eory
- SRUC, West Mains Rd, Edinburgh, EH9 3JG, UK
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Antle JM, Basso B, Conant RT, Godfray HCJ, Jones JW, Herrero M, Howitt RE, Keating BA, Munoz-Carpena R, Rosenzweig C, Tittonell P, Wheeler TR. Towards a new generation of agricultural system data, models and knowledge products: Design and improvement. AGRICULTURAL SYSTEMS 2017; 155:255-268. [PMID: 28701817 PMCID: PMC5485644 DOI: 10.1016/j.agsy.2016.10.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Revised: 10/01/2016] [Accepted: 10/04/2016] [Indexed: 05/11/2023]
Abstract
This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.
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Janssen SJ, Porter CH, Moore AD, Athanasiadis IN, Foster I, Jones JW, Antle JM. Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology. AGRICULTURAL SYSTEMS 2017; 155:200-212. [PMID: 28701813 PMCID: PMC5485661 DOI: 10.1016/j.agsy.2016.09.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 09/14/2016] [Accepted: 09/21/2016] [Indexed: 05/08/2023]
Abstract
Agricultural modeling has long suffered from fragmentation in model implementation. Many models are developed, there is much redundancy, models are often poorly coupled, model component re-use is rare, and it is frequently difficult to apply models to generate real solutions for the agricultural sector. To improve this situation, we argue that an open, self-sustained, and committed community is required to co-develop agricultural models and associated data and tools as a common resource. Such a community can benefit from recent developments in information and communications technology (ICT). We examine how such developments can be leveraged to design and implement the next generation of data, models, and decision support tools for agricultural production systems. Our objective is to assess relevant technologies for their maturity, expected development, and potential to benefit the agricultural modeling community. The technologies considered encompass methods for collaborative development and for involving stakeholders and users in development in a transdisciplinary manner. Our qualitative evaluation suggests that as an overall research challenge, the interoperability of data sources, modular granular open models, reference data sets for applications and specific user requirements analysis methodologies need to be addressed to allow agricultural modeling to enter in the big data era. This will enable much higher analytical capacities and the integrated use of new data sources. Overall agricultural systems modeling needs to rapidly adopt and absorb state-of-the-art data and ICT technologies with a focus on the needs of beneficiaries and on facilitating those who develop applications of their models. This adoption requires the widespread uptake of a set of best practices as standard operating procedures.
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Affiliation(s)
- Sander J.C. Janssen
- Wageningen University and Research Centre, Postbus 47, 6700AA Wageningen, Netherlands
| | - Cheryl H. Porter
- Agricultural & Biological Engineering, University of Florida, PO Box 110570, Gainesville, FL 32611, USA
| | - Andrew D. Moore
- CSIRO Agriculture & Food, GPO Box 1600, Canberra, ACT, 2601, Australia
| | | | - Ian Foster
- Computation Institute, University of Chicago, IL, USA
| | - James W. Jones
- Agricultural & Biological Engineering, University of Florida, PO Box 110570, Gainesville, FL 32611, USA
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Fürst C, Frank S, Witt A, Koschke L, Makeschin F. Assessment of the effects of forest land use strategies on the provision of ecosystem services at regional scale. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2013; 127 Suppl:S96-S116. [PMID: 23158524 DOI: 10.1016/j.jenvman.2012.09.020] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2012] [Revised: 07/23/2012] [Accepted: 09/18/2012] [Indexed: 06/01/2023]
Abstract
This paper presents results of a case study in Middle Saxony, Germany, where the impact of conversion, afforestation and alternatively introduction of short rotation coppice areas on the provision of ecosystem services was tested in a spatially inexplicit and a spatially explicit way to formulate recommendations for regional planning. While the spatially inexplicit testing did not lead to clear results regarding to what degree forests or short rotation coppice areas are desirable and applicable, the spatially explicit testing revealed that an increase in the forest area or area with short rotation coppice by 29.7% in unstructured agriculturally dominated Loess regions, 14.4% in more topographically structured parts in the North-East of the model region and 23.6% in its mountainous parts would be beneficial. Potentially resulting losses in the provision of bioresources and regional economy can be considerably reduced by replacing afforestation areas with short rotation coppice. In summary, we found that the spatially explicit analysis of land use scenarios in combination with a more detailed land use classification and including an assessment of changes in land use pattern gave us an improved basis for assessing different possible planning strategies and to enhance the communication between forest management planners and regional planners.
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Affiliation(s)
- Christine Fürst
- Centre for Development Research, Rheinische Friedrich-Wilhelm University Bonn, Walter Flex Strasse 3, Bonn, Germany.
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Huang L, Ban J, Han YT, Yang J, Bi J. Multi-angle indicators system of non-point pollution source assessment in rural areas: a case study near Taihu Lake. ENVIRONMENTAL MANAGEMENT 2013; 51:939-950. [PMID: 23456193 DOI: 10.1007/s00267-013-0024-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2012] [Accepted: 02/04/2013] [Indexed: 06/01/2023]
Abstract
This study aims to identify key environmental risk sources contributing to water eutrophication and to suggest certain risk management strategies for rural areas. The multi-angle indicators included in the risk source assessment system were non-point source pollution, deficient waste treatment, and public awareness of environmental risk, which combined psychometric paradigm methods, the contingent valuation method, and personal interviews to describe the environmental sensitivity of local residents. Total risk values of different villages near Taihu Lake were calculated in the case study, which resulted in a geographic risk map showing which village was the critical risk source of Taihu eutrophication. The increased application of phosphorus (P) and nitrogen (N), loss vulnerability of pollutant, and a lack of environmental risk awareness led to more serious non-point pollution, especially in rural China. Interesting results revealed by the quotient between the scores of objective risk sources and subjective risk sources showed what should be improved for each study village. More environmental investments, control of agricultural activities, and promotion of environmental education are critical considerations for rural environmental management. These findings are helpful for developing targeted and effective risk management strategies in rural areas.
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Affiliation(s)
- Lei Huang
- School of the Environment, Nanjing University, Xianlin Campus, 163 Xianlin Avenue, Nanjing, 210023, Jiangsu, China.
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