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de Visser, C.; Schoorlemmer, H.; Golaszewski, J.; Olba-Ziety, E.; Stolarski, M.; Brodzinski, Z.; Myhan, R.; Baptista, F.; Silva, L.L.; Murcho, D.; de Castro Neto, M.; Meyer-Aurich, A.; Briassoulis, D.P., P.; Balafoutis, A.; Lutsyuk, C.; Dalgaard, T. |
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Agenda for Transnational Co-operation on energy efficiency in agriculture |
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Report |
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2013 |
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Project deliverable report 4.5. FP7 EU project: Agriculture & Energy Efficiency AGREE, www.agree.aua.gr. |
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LiveM |
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Wageningen |
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Wageningen UR |
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MA @ admin @ |
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2071 |
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Sanz-Cobena, A.; Lassaletta, L.; Gamier, J.; Smith, P.; Sanz-Cobena, A.; Lassaletta, L.; Gamier, J.; Smith, P. |
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Mitigation and quantification of greenhouse gas emissions in Mediterranean cropping systems |
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2017 |
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Agriculture, Ecosystems & Environment |
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Agriculture, Ecosystems & Environment |
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238 |
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1-4 |
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Climate-Change; Soil Carbon |
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2017-03-23 |
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0167-8809 |
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Editorial Material |
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CropM, ft_MACSUR |
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MA @ admin @ |
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4940 |
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Mansouri, M.; Dumont, B.; Destain, M.-F. |
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Title |
Modeling and prediction of nonlinear environmental system using Bayesian methods |
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2013 |
Publication |
Computers and Electronics in Agriculture |
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Computers and Electronics in Agriculture |
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92 |
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16-31 |
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state and parameter estimation; variational filter; particle filter; extended kalman filter; nonlinear environmental system; leaf area index and soil moisture model; extended kalman filter; state-space models; parameter-estimation; particle filters; navigation; tutorial; tracking |
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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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0168-1699 |
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CropM |
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MA @ admin @ |
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4495 |
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Author |
Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions |
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Journal Article |
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Year |
2015 |
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Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
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16 |
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4 |
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361-384 |
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nitrogen management; climatic variability; lars-wg weather generator; stics soil-crop model; pearson system; probability risk assessment; crop model stics; fertilizer nitrogen; generic model; wheat yield; maize; simulation; skewness; field; agriculture; scenarios |
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At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha(-1)), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of -1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha(-1)) of the distribution. |
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1385-2256 |
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CropM, ft_macsur |
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MA @ admin @ |
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4519 |
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Bonatti, M.; Schlindwein, S.L.; De Vasconcelos, A.C.F.; Sieber, S.; Agostini, L.R.D.; Lana, M.A.; Fantini, A.C.; Homem, L.H.I.; Canci, A. |
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Social organization and agricultural strategies to face climate variability: a case study in Guaraciaba, southern Brazil |
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2013 |
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Sustainable Agriculture Research |
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Sustainable Agriculture Research |
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2 |
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3 |
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118 |
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Climate scenarios and projections have suggested that the impacts of climate change on land use will be noticed particularly by the communities that depend on natural resources for their subsistence. The climate vulnerability of poor communities varies greatly, but in general, climate change combines with other threats and becomes superimposed on existing vulnerabilities. This paper presents a case study that strives to understand the social organization in a vulnerable community of Guaraciaba, in southern Brazil, to investigate aspects of an adaptation strategy to climate change based on the local development and conservation of landraces of a set of crop species. Landraces are varieties better adapted to adversities, especially drought, which is an important threat to the famers in the region. Every farmer receives annually a “kit of biodiversity”, a set of local varieties with the amount of seeds necessary to be cultivated in order to produce enough food for the family. The study had a qualitative approach and was carried out through semi-structured interviews with technicians and 30% of the rural families who farm with landraces. The study concludes that the factors that make this adaptation strategy sustainable are: the ability to undertake actions strongly based on local socio-cultural needs (a social support network), biodiversity management practices designed to reduce external economic dependence, self management of genetic resources, the establishment of priorities based on locally available resources, a work plan for community participation (field days, a community based festival), the establishment of the roles of community in the planning and implementation of programs for biodiversity management. |
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1927-0518 |
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TradeM, ftnotmacsur |
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MA @ admin @ |
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4600 |
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