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Allan, C.; Nguyen, T.P.L.; Seddaiu, G.; Wilson, B.; Roggero, P.P. |
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Title |
Integrating local knowledge with experimental research: case studies on managing cropping systems in Italy and Australia |
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Journal Article |
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Year |
2013 |
Publication |
Italian Journal of Agronomy |
Abbreviated Journal |
Ital. J. Agron. |
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Volume |
8 |
Issue |
2 |
Pages |
15 |
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Keywords |
participatory action research; agronomic research; local knowledge; knowledge integration |
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Abstract  |
The sustainable development of agricultural systems is currently challenged by many complex agro-environmental issues. These are characterized by an incomplete understanding of the situation and the problems that arise, and the conflicting opinions that result, issues over boundaries that are often difficult to define, and controversy over the multiple goals and uncertain outcomes. Added to these characteristics, we also have the slow and often inadequate uptake and implementation of research outcomes in this complex, real world. In order to improve sustainability of agro-ecosystems, agronomic research must move away from the linear research approaches and extension practices adopted so far that have focused purely on biophysical agro-ecosystems. The theoretical operational space of agronomic research must be transformed by considering agronomic issues as part of a broader social-agro-ecosystem. One aspect of this transformation is the inclusion of knowledge collected on a local level with the participation of farmers on the ground. The integration of local experiential knowledge with traditional agronomic research is by necessity based on the participation of many different stakeholders and there can be no single blueprint for how best to develop and use the input received. However, agronomists and policy advisors require general guidelines drawn up from actual experience in order to accelerate positive agronomic change. We address this need through a comparative analysis of two case studies; one involves multi-stakeholder research in a cropping system in the dairy district of Arborea, Sardinia, Italy. The central question was: How can high crop production be maintained while also achieving the EU target water quality and minimizing the production costs? The second case is a multi-stakeholder soil health project from south-eastern Australia. Here the central question was: How can soil decline be prevented and reversed in this district, and soils made more resilient to future challenges? The Social Learning for the Integrated Management and sustainable use of water (SLIM) framework, a useful heuristic tool for exploring the dynamics of transformational change, guided the analysis of the case studies. Within this framework, a key indicator of success is the emergence of new knowledge from the creation of new spaces for learning between researchers and local stakeholders. The Italian case study appears to have been the most successful in this sense, as opportunities for joint exploration of research data allowed new potential farming responses to the central question to emerge. The multi-stakeholder processes in the Australian case focused more on providing public openings for individual learning, and missed the opportunity for new knowledge to emerge through joint exploration. We conclude that participatory approaches may enable transformative practice through knowledge integration, but that this process is not an automatic outcome of increased community participation. |
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2039-6805 1125-4718 |
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CropM |
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MA @ admin @ |
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4482 |
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Robinson, S.; van Meijl, H.; Willenbockel, D.; Valin, H.; Fujimori, S.; Masui, T.; Sands, R.; Wise, M.; Calvin, K.; Havlik, P.; Mason d’Croz, D.; Tabeau, A.; Kavallari, A.; Schmitz, C.; Dietrich, J.P.; von Lampe, M. |
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Title |
Comparing supply-side specifications in models of global agriculture and the food system |
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Journal Article |
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Year |
2014 |
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Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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45 |
Issue |
1 |
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21-35 |
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Keywords |
global agricultural models; global food system scenario analysis; general equilibrium; partial equilibrium; growth; trade |
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This article compares the theoretical and functional specification of production in partial equilibrium (PE) and computable general equilibrium (CGE) models of the global agricultural and food system included in the AgMIP model comparison study. The two model families differ in their scopepartial versus economy-wideand in how they represent technology and the behavior of supply and demand in markets. The CGE models are deep structural models in that they explicitly solve the maximization problem of consumers and producers, assuming utility maximization and profit maximization with production/cost functions that include all factor inputs. The PE models divide into two groups on the supply side: (1) shallow structural models, which essentially specify area/yield supply functions with no explicit maximization behavior, and (2) deep structural models that provide a detailed activity-analysis specification of technology and explicit optimizing behavior by producers. While the models vary in their specifications of technology, both within and between the PE and CGE families, we consider two stylized theoretical models to compare the behavior of crop yields and supply functions in CGE models with their behavior in shallow structural PE models. We find that the theoretical responsiveness of supply to changes in prices can be similar, depending on parameter choices that define the behavior of implicit supply functions over the domain of applicability defined by the common scenarios used in the AgMIP comparisons. In practice, however, the applied models are more complex and differ in their empirical sensitivity to variations in specificationcomparability of results given parameter choices is an empirical question. To illustrate the issues, sensitivity analysis is done with one global CGE model, MAGNET, to indicate how the results vary with different specification of technical change, and how they compare with the results from PE models. |
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0169-5150 |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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4735 |
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Mansouri, M.; Destain, M.-F. |
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Title |
Predicting biomass and grain protein content using Bayesian methods |
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Journal Article |
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2015 |
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Stochastic Environmental Research and Risk Assessment |
Abbreviated Journal |
Stoch. Environ. Res. Risk Assess. |
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29 |
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4 |
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1167-1177 |
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crop model; particle filter; prediction; ensemble kalman filter; parameter-estimation; particle filters; decision-support; state estimation; model; nitrogen; navigation; tracking; systems |
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This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback-Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times. |
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1436-3240 1436-3259 |
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CropM |
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MA @ admin @ |
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4664 |
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Valin, H.; Sands, R.D.; van der Mensbrugghe, D. and; Nelson, G.C.; Ahammad, H.; Blanc, E.; Bodirsky; Benjamin; Fujimori, S.; Hasegawa, T.; Havlik, P.; and Heyhoe, E.; Kyle, P.; Mason-D’Croz, D.; Paltsev; Sergey; Rolinski, S.; Tabeau, A.; van Meijl, H. and; von Lampe, M.; Willenbockel, D. |
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The future of food demand: Understanding differences in global economic models |
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Journal Article |
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Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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45 |
Issue |
1 |
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51-67 |
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Keywords |
world food demand; socioeconomic pathways; climate change; computable general equilibrium; partial equilibrium; systems |
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Understanding the capacity of agricultural systems to feed the world population under climate change requires projecting future food demand. This article reviews demand modeling approaches from 10 global economic models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP). We compare food demand projections in 2050 for various regions and agricultural products under harmonized scenarios of socioeconomic development, climate change, and bioenergy expansion. In the reference scenario (SSP2), food demand increases by 59-98% between 2005 and 2050, slightly higher than the most recent FAO projection of 54% from 2005/2007. The range of results is large, in particular for animal calories (between 61% and 144%), caused by differences in demand systems specifications, and in income and price elasticities. The results are more sensitive to socioeconomic assumptions than to climate change or bioenergy scenarios. When considering a world with higher population and lower economic growth (SSP3), consumption per capita drops on average by 9\% for crops and 18% for livestock. The maximum effect of climate change on calorie availability is -6% at the global level, and the effect of biofuel production on calorie availability is even smaller. |
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0169-5150 |
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TradeM, ft_macsur |
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MA @ admin @ |
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4752 |
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Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables |
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Journal Article |
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2015 |
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Climate Research |
Abbreviated Journal |
Clim. Res. |
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65 |
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141-157 |
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crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i |
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We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area. |
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0936-577x |
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CropM, ft_macsur |
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MA @ admin @ |
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4754 |
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