| Records |
| Author |
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. |
| Title |
Social organization and agricultural strategies to face climate variability: a case study in Guaraciaba, southern Brazil |
Type |
Journal Article |
| Year |
2013 |
Publication  |
Sustainable Agriculture Research |
Abbreviated Journal |
Sustainable Agriculture Research |
| Volume |
2 |
Issue |
3 |
Pages |
118 |
| Keywords |
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| Abstract |
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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English |
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| ISSN |
1927-0518 |
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| Notes |
TradeM, ftnotmacsur |
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no |
| Call Number |
MA @ admin @ |
Serial |
4600 |
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| Author |
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. |
| Title |
Agenda for Transnational Co-operation on energy efficiency in agriculture |
Type |
Report |
| Year |
2013 |
Publication |
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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Place of Publication |
Wageningen |
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Wageningen UR |
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no |
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MA @ admin @ |
Serial |
2071 |
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| Author |
Mansouri, M.; Dumont, B.; Leemans, V.; Destain, M.-F. |
| Title |
Bayesian methods for predicting LAI and soil water content |
Type |
Journal Article |
| Year |
2014 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
| Volume |
15 |
Issue |
2 |
Pages |
184-201 |
| Keywords |
crop model; bayes; data assimilation; extended kalman filtering; particle filtering; variational filtering; leaf-area index; parameter-estimation; crop models; moisture; instruments; management; sensors; state |
| Abstract |
LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: extended Kalman filtering (EKF), particle filtering (PF), and variational filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation. |
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English |
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1385-2256 |
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Article |
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| Notes |
CropM, ftnotmacsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4629 |
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| Author |
Dumont, B.; Leemans, V.; Ferrandis, S.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
| Title |
Assessing the potential of an algorithm based on mean climatic data to predict wheat yield |
Type |
Journal Article |
| Year |
2014 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
| Volume |
15 |
Issue |
3 |
Pages |
255-272 |
| Keywords |
stics model; yield prediction; real-time; proxy-sensing; stochastic weather generator; crop yield; mediterranean environment; simulation-model; variability; nitrogen; ensembles; forecasts; demeter; europe |
| Abstract |
The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days. |
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English |
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1385-2256 1573-1618 |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4621 |
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| Author |
Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
| Title |
Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions |
Type |
Journal Article |
| Year |
2015 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
| Volume |
16 |
Issue |
4 |
Pages |
361-384 |
| Keywords |
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 |
| Abstract |
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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English |
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1385-2256 |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4519 |
| Permanent link to this record |
