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Mansouri, M., Dumont, B., & Destain, M. - F. (2013). Modeling and prediction of nonlinear environmental system using Bayesian methods. Computers and Electronics in Agriculture, 92, 16–31.
Abstract: An environmental dynamic system is usually modeled as a nonlinear system described by a set of nonlinear ODEs. A central challenge in computational modeling of environmental systems is the determination of the model parameters. In these cases, estimating these variables or parameters from other easily obtained measurements can be extremely useful. This work addresses the problem of monitoring and modeling a leaf area index and soil moisture model (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), particle filter (PF), and the more recently developed technique variational filter (VF). Specifically, two comparative studies are performed. In the first comparative study, the state variables (the leaf-area index LAI, the volumetric water content of the soil layer 1, HUR1 and the volumetric water content of the soil layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of number of estimated model parameters on the accuracy and convergence of these techniques are also assessed. The results of both comparative studies show that the PF provides a higher accuracy than the EKF, which is due to the limited ability of the EKF to handle highly nonlinear processes. The results also show that the VF provides a significant improvement over the PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the VF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The results of the second comparative study show that, for all techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. However, the VF can still provide both convergence as well as accuracy related advantages over other estimation methods. (C) 2013 Elsevier B.V. All rights reserved.
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Irz, X., & Kuosmanen, N. (2013). Explaining growth in demand for dairy products in Finland: an econometric analysis. Food Economics, 9(sup5), 47–56.
Abstract: The dairy sector represents the cornerstone of Finnish agriculture but faces new challenges linked to the decoupling of farm subsidies and abolition of milk production quotas. Because of its increasing exposure to market forces, the sector must anticipate future changes in demand and deliver precisely what Finnish consumers want. This paper contributes to that goal by analyzing retroactively the drivers of demand for dairy products over the period 1975–2010 using National Accounts Data. After presenting the evolution of consumption for dairy products, we estimate a complete system of demand for food and dairy products and use it to decompose demand growth into a substitution effect, income effect, and trend effect. The analysis points to the severity of the challenges that the sector is facing. Stagnant consumption is at least partially the result of continuous but adverse taste changes, and as Finnish consumers grow more prosperous, they allocate an increasingly smaller share of their food budget to the dairy group. The low own-price elasticity of demand for dairy products also limits the benefits to the sector of growth in milk production. Hence, business-as-usual will result in the dwindling importance of the dairy sector in the Finnish food chain. Innovation and product differentiation, perhaps emphasizing the attributes of livestock production processes, are clearly required to counter this evolution.
Keywords: Consumption; food; almost ideal demand system; decomposition; elasticities; milk; demand analysis; farm
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Bressan, R. A., Park, H. C., Orsini, F., Oh, D. -ha, Dassanayake, M., Inan, G., et al. (2013). Biotechnology for mechanisms that counteract salt stress in extremophile species: a genome-based view. Plant Biotechnol. Rep., 7(1), 27–37.
Abstract: Molecular genetics has confirmed older research and generated new insights into the ways how plants deal with adverse conditions. This body of research is now being used to interpret stress behavior of plants in new ways, and to add results from most recent genomics-based studies. The new knowledge now includes genome sequences of species that show extreme abiotic stress tolerances, which enables new strategies for applications through either molecular breeding or transgenic engineering. We will highlight some physiological features of the extremophile lifestyle, outline emerging features about halophytism based on genomics, and discuss conclusions about underlying mechanisms.
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Mandryk, M., Reidsma, P., Kanellopoulos, A., Groot, J. C. J., & van Ittersum, M. K. (2014). The role of farmers’ objectives in current farm practices and adaptation preferences: a case study in Flevoland, the Netherlands. Reg Environ Change, 14(4), 1463–1478.
Abstract: The diversity in farmers’ objectives and responses to external drivers is usually not considered in integrated assessment studies that investigate impacts and adaptation to climate and socio-economic change. Here, we present an approach to assess how farmers’ stated objectives relate to their currently implemented practices and to preferred adaptation options, and we discuss what this implies for assessments of future changes. We based our approach on a combination of multi-criteria decision-making methods. We consistently assessed the importance of farmers’ objectives and adaptation preferences from what farmers say (based on interviews), from what farmers actually do (by analysing current farm performance) and from what farmers want (through a selected alternative farm plan). Our study was performed for six arable farms in Flevoland, a province in the Netherlands. Based on interviews with farmers, we reduced the long list of possible objectives to the most important ones. The objectives we assessed included maximization of economic result and soil organic matter, and minimization of gross margin variance, working hours and nitrogen balance. In our sample, farmers’ stated preferences in objectives were often not fully reflected in realized farming practices. Adaptation preferences of farmers largely resembled their current performance, but generally involved a trend towards stated preferences. Our results suggest that in Flevoland, although farmers do have more objectives, in practical decision-making they focus on economic result maximization, while for strategic decision-making they account for objectives influencing long-term performance and indicators associated with sustainability, in this case soil organic matter.
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Nelson, G. C., van der Mensbrugghe, D., Ahammad, H., Blanc, E., Calvin, K., Hasegawa, T., et al. (2014). Agriculture and climate change in global scenarios: why don’t the models agree. Agric. Econ., 45(1), 85.
Abstract: Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty.
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