AgroTutor: A Mobile Phone Application Supporting Sustainable Agricultural Intensification
Abstract
:1. Introduction
2. The AgroTutor Mobile Application
2.1. Information Collected from the Farmers
2.2. Information Provided to the Farmers
2.2.1. Weather Information
2.2.2. Historical Yield Potential
2.2.3. Benchmarking Local Information
2.2.4. Windows of Opportunity
2.2.5. Recommended Agricultural Practices
2.2.6. Commodity Price Forecasting
2.2.7. Communication, Data Recording, Accessibility, and User Experience
3. Preliminary Tests and Farmers Willingness to Adopt
4. AgroTutor and the Sustainable Development Goals: Current and Potential Contributions
5. AgroTutor and Crowdsourcing with Small Farmers
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Garnett, T.; Appleby, M.C.; Balmford, A.; Bateman, I.J.; Benton, T.G.; Bloomer, P.; Burlingame, B.; Dawkins, M.; Dolan, L.; Fraser, D.; et al. Sustainable Intensification in Agriculture: Premises and Policies. Science 2013, 341, 33–34. [Google Scholar] [CrossRef] [PubMed]
- Tilman, D.; Balzer, C.; Hill, J.; Befort, B.L. Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. USA 2011, 108, 20260–20264. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Matson, P.A.; Vitousek, P.M. Agricultural Intensification: Will Land Spared from Farming be Land Spared for Nature? Conserv. Biol. 2006, 20, 709–710. [Google Scholar] [CrossRef] [PubMed]
- Struik, P.C.; Bruun, S. Sustainable intensification in agriculture: The richer shade of green. A review. Agron. Sustain. Dev. 2017, 37, 39. [Google Scholar] [CrossRef]
- Hellin, J.; Camacho, C. Agricultural research organisations’ role in the emergence of agricultural innovation systems. Dev. Pract. 2016, 27, 111–115. [Google Scholar] [CrossRef]
- Howe, J. The rise of crowdsourcing. Wired Mag. 2006, 14, 1–4. [Google Scholar]
- Estellés-Arolas, E.; González, L.D.G.F. Towards an integrated crowdsourcing definition. J. Inf. Sci. 2012, 38, 189–200. [Google Scholar] [CrossRef] [Green Version]
- Hall, G. Pro WPF and Silverlight MVVM: Effective Application Development with Model-View-ViewModel, 1st ed.; Apress: Berkely, CA, USA, 2010; ISBN 1430231629/9781430231622. [Google Scholar]
- Ruiz, J.A.; Sanchez, J.J.; Goodman, M.M. Base temperature and heat unit requirement of 49 Mexican maize races. Maydica 1998, 43, 277–282. [Google Scholar]
- Capristo, P.R.; Rizzalli, R.H.; Andrade, F.H. Ecophysiological Yield Components of Maize Hybrids with Contrasting Maturity. Agron. J. 2007, 99, 1111–1118. [Google Scholar] [CrossRef]
- Ruane, A.C.; Goldberg, R.; Chryssanthacopoulos, J. Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agric. For. Meteorol. 2015, 200, 233–248. [Google Scholar] [CrossRef] [Green Version]
- Jarvis, A.; Guevara, E.; Reuter, H.I.; Nelson, A.D. Hole-Filled SRTM for the Globe Version 4. 2008. Available online: http://srtm.csi.cgiar.org/ (accessed on 8 November 2020).
- Lazos, E.; Chauvet, M. Análisis del contexto social y biocultural de las colectas de maíces nativos en México. In Proyecto Global de Maíces Nativos. Informe de Gestión; CONABIO: Mexico City, Mexico, 2011. [Google Scholar]
- Williams, J.R. The EPIC Model. In Computer Models of Watershed Hydrology; Water Resources Publications: Littleton, CO, USA, 1995; pp. 909–1000. ISBN 0918334918. [Google Scholar]
- Balkovič, J.; Van Der Velde, M.; Skalský, R.; Xiong, W.; Folberth, C.; Khabarov, N.; Smirnov, A.; Mueller, N.D.; Obersteiner, M. Global wheat production potentials and management flexibility under the representative concentration pathways. Glob. Planet. Chang. 2014, 122, 107–121. [Google Scholar] [CrossRef] [Green Version]
- Deschamps, S.L. Cosechando Innovación. Un Modelo de México Para el Mundo, Maíz y Trigo; Innovagro: Villa Guerrero, Mexico, 2016. [Google Scholar]
- Ciampitti, I.A.; Vyn, T.J. Nutrient Sufficiency Concepts for Modern Corn Hybrids: Impacts of Management Practices and Yield Levels. Crop. Manag. 2014, 13. [Google Scholar] [CrossRef] [Green Version]
- Gramig, B.M.; Massey, R.; Yun, S.D. Nitrogen application decision-making under climate risk in the U.S. Corn Belt. Clim. Risk Manag. 2017, 15, 82–89. [Google Scholar] [CrossRef] [Green Version]
- De Oliveira, S.M.; De Almeida, R.E.M.; Ciampitti, I.A.; Junior, C.P.; Lago, B.C.; Trivelin, P.C.O.; Favarin, J.L. Understanding N timing in corn yield and fertilizer N recovery: An insight from an isotopic labeled-N determination. PLoS ONE 2018, 13, e0192776. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bonhomme, R. Bases and limits to using ‘degree.day’ units. Eur. J. Agron. 2000, 13, 1–10. [Google Scholar] [CrossRef]
- Delerce, S.; Dorado, H.; Grillon, A.; Rebolledo, M.C.; Prager, S.D.; Patiño, V.H.; Varón, G.G.; Jiménez, D. Assessing weather-yield relationships in rice at local scale using data mining approaches. PLoS ONE 2016, 11, e0161620. [Google Scholar] [CrossRef] [Green Version]
- Jiménez, D.; Dorado, H.; Cock, J.; Prager, S.D.; Delerce, S.; Grillon, A.; Bejarano, M.A.; Benavides, H.; Jarvis, A. From observation to information: Data-driven understanding of on farm yield variation. PLoS ONE 2016, 11, e0150015. [Google Scholar] [CrossRef] [Green Version]
- Crespo Cuaresma, J.; Hlouskova, J.; Obersteiner, M. Fundamentals, speculation or macroeconomic conditions? Modelling and forecasting Arabica coffee prices. Eur. Rev. Agric. Econ. 2018, 45, 583–615. [Google Scholar] [CrossRef]
- Crespo Cuaresma, J.; Hlouskova, J.; Obersteiner, M. Forecasting Commodity Prices under Specification Uncertainty: A Comprehensive Approach. Deliverable No. 8.3. In Metrics, Models and Foresight for European Sustainable Food And Nutrition Security; SUSFANS: The Hague, The Netherlands, 2017. [Google Scholar]
- Harwin, K.; Gandhi, R. Digital Green: A Rural Video-Based Social Network for Farmer Training (Innovations Case Narrative: Digital Green). Innov. Technol. Gov. Glob. 2014, 9, 53–61. [Google Scholar] [CrossRef]
- Venkatesh, V.; Thong, J.Y.V.; Xu, X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012, 36, 157. [Google Scholar] [CrossRef] [Green Version]
- Venkatesh, V.; Morris, M.G. Davis User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425. [Google Scholar] [CrossRef] [Green Version]
- Beza, E.; Reidsma, P.; Poortvliet, P.M.; Belay, M.M.; Bijen, B.S.; Kooistra, L. Exploring farmers’ intentions to adopt mobile Short Message Service (SMS) for citizen science in agriculture. Comput. Electron. Agric. 2018, 151, 295–310. [Google Scholar] [CrossRef]
- Fraisl, D.; Campbell, J.; See, L.; Wehn, U.; Wardlaw, J.; Gold, M.; Moorthy, I.; Arias, R.; Piera, J.; Oliver, J.L.; et al. Mapping citizen science contributions to the UN sustainable development goals. Sustain. Sci. 2020, 1–17. [Google Scholar] [CrossRef]
- FAO. The Voluntary Guidelines on the Responsible Governance of Tenure of Land, Fisheries and Forests in the Context of National Food Security; FAO: Rome, Italy, 2012. [Google Scholar]
- INEGI. Encuesta Nacional Agropecuaria 2017; INEGI: Aguascalientes, Mexico, 2018; Volume 1, p. 24.
- Kanter, D.R.; Musumba, M.; Wood, S.L.; Palm, C.; Antle, J.; Balvanera, P.; Dale, V.H.; Havlik, P.; Kline, K.L.; Scholes, R.; et al. Evaluating agricultural trade-offs in the age of sustainable development. Agric. Syst. 2018, 163, 73–88. [Google Scholar] [CrossRef]
- Terlau, W.; Hirsch, D.; Blanke, M. Smallholder farmers as a backbone for the implementation of the Sustainable Development Goals. Sustain. Dev. 2018, 27, 523–529. [Google Scholar] [CrossRef]
- Minet, J.; Curnel, Y.; Gobin, A.; Goffart, J.-P.; Mélard, F.; Tychon, B.; Wellens, J.; Defourny, P. Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach. Comput. Electron. Agric. 2017, 142, 126–138. [Google Scholar] [CrossRef] [Green Version]
Maturity Class | PHU/GDD [°C] | Climate Suitability | Tbase [°C] |
---|---|---|---|
Early | 1680 | Cold | 4 |
Mid-early | 1890 | Temperate/subtropical | 7 |
Intermediate | 2100 | Tropical | 9 |
Mid-late | 2310 | Hybrid | 10 |
Late | 2520 |
Positive Characteristics | Challenges and Suggestions |
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Laso Bayas, J.C.; Gardeazabal, A.; Karner, M.; Folberth, C.; Vargas, L.; Skalský, R.; Balkovič, J.; Subash, A.; Saad, M.; Delerce, S.; et al. AgroTutor: A Mobile Phone Application Supporting Sustainable Agricultural Intensification. Sustainability 2020, 12, 9309. https://doi.org/10.3390/su12229309
Laso Bayas JC, Gardeazabal A, Karner M, Folberth C, Vargas L, Skalský R, Balkovič J, Subash A, Saad M, Delerce S, et al. AgroTutor: A Mobile Phone Application Supporting Sustainable Agricultural Intensification. Sustainability. 2020; 12(22):9309. https://doi.org/10.3390/su12229309
Chicago/Turabian StyleLaso Bayas, Juan Carlos, Andrea Gardeazabal, Mathias Karner, Christian Folberth, Luis Vargas, Rastislav Skalský, Juraj Balkovič, Anto Subash, Moemen Saad, Sylvain Delerce, and et al. 2020. "AgroTutor: A Mobile Phone Application Supporting Sustainable Agricultural Intensification" Sustainability 12, no. 22: 9309. https://doi.org/10.3390/su12229309