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Wolf, J., Ouattara, K., & Supit, I. (2015). Sowing rules for estimating rainfed yield potential of sorghum and maize in Burkina Faso. Agricultural and Forest Meteorology, 214-215, 208–218.
Abstract: To reduce the dependence on local expert knowledge, which is important for large-scale crop modelling studies, we analyzed sowing dates and rules for maize (Zea mays L.) and sorghum (Sorghum bicolor (L)) at three locations in Burkina Faso with strongly decreasing rainfall amounts from south to north. We tested in total 22 methods to derive optimal sowing dates that result in highest water-limited yields and lowest yield variation in a reproducible and objective way. The WOFOST crop growth simulation model was used. We found that sowing dates that are based on local expert knowledge, may work quite well for Burkina Faso and for West Africa in general. However, when no a priori information is available, maize should be sown between Julian days 160 and 200, with application of the following criteria: (a) cumulative rainfall in the sowing window is >= 3 cm or available soil moisture content is >2 cm in the moderately dry central part of Burkina Faso, (b) cumulative rainfall in this period is >= 2 cm or available soil moisture content is >1 cm in the more humid regions in the southern part of Burkina Faso. Sorghum should also be sown between Julian days 160 and 200 with application of the following criteria: (a) in the dry northern part of Burkina Faso the long duration sorghum variety should be sown when cumulative rainfall is >2 cm in the sowing window, and the short duration sorghum variety should be sown later when cumulative rainfall is >= 3 cm, (b) in central Burkina Faso sowing should start when cumulative rainfall in this period is >= 2 cm or when available soil moisture content is >1 cm. Sowing date rules are shown to be generally crop and location specific and are not generic for West Africa. However, the required precision of the sowing rules appears to rapidly decrease with increasing duration and intensity of the rainy season. Sowing delay as a result of, for example, labour constraints, has a disastrous effect on rainfed maize and sorghum yields, particularly in the northern part of West Africa with low rainfall. Optimization of sowing dates can also be done by simulating crop yields in a time window of two months around a predefined sowing date. Using these optimized dates appears to result in a good estimate of the maximal mean rainfed yield level. (C) 2015 Elsevier B.V. All rights reserved.
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Caubel, J., García de Cortázar-Atauri, I., Launay, M., de Noblet-Ducoudré, N., Huard, F., Bertuzzi, P., et al. (2015). Broadening the scope for ecoclimatic indicators to assess crop climate suitability according to ecophysiological, technical and quality criteria. Agricultural and Forest Meteorology, 207, 94–106.
Abstract: The cultivation of crops in a given area is highly dependent of climatic conditions. Assessment of how the climate is favorable is highly useful for planners, land managers, farmers and plant breeders who can propose and apply adaptation strategies to improve agricultural potentialities. The aim of this study was to develop an assessment method for crop-climate suitability that was generic enough to be applied to a wide range of issues and crops. The method proposed is based on agroclimatic indicators that are calculated over phenological periods (ecoclimatic indicators). These indicators are highly relevant since they provide accurate information about the effect of climate on particular plant processes and cultural practices that take place during specific phenological periods. Three case studies were performed in order to illustrate the potentialities of the method. They concern annual (maize and wheat) and perennial (grape) crops and focus on the study of climate suitability in terms of the following criteria: ecophysiological, days available to carry out cultural practices, and harvest quality. The analysis of the results revealed both the advantages and limitations of the method. The method is general and flexible enough to be applied to a wide range of issues even if an expert assessment is initially needed to build the analysis framework. The limited number of input data makes it possible to use it to explore future possibilities for agriculture in many areas. The access to intermediate information through elementary ecoclimatic indicators allows users to propose targeted adaptations when climate suitability is not satisfactory.
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Gomara, I., Bellocchi, G., Martin, R., Rodriguez-Fonseca, B., & Ruiz-Ramos, M. (2020). Influence of climate variability on the potential forage production of a mown permanent grassland in the French Massif Central. Agricultural and Forest Meteorology, 280, 107768.
Abstract: Climate Services (CS) provide support to decision makers across socio-economic sectors. In the agricultural sector, one of the most important CS applications is to provide timely and accurate yield forecasts based on climate prediction. In this study, the Pasture Simulation model (PaSim) was used to simulate, for the period 1959–2015, the forage production of a mown grassland system (Laqueuille, Massif Central of France) under different management conditions, with meteorological inputs extracted from the SAFRAN atmospheric database. The aim was to generate purely climate-dependent timeseries of optimal forage production, a variable that was maximized by brighter and warmer weather conditions at the grassland. A long-term increase was observed in simulated forage yield, with the 1995–2015 average being 29% higher than the 1959–1979 average. Such increase seems consistent with observed rising trends in temperature and CO2, and multi-decadal changes in incident solar radiation. At interannual timescales, sea surface temperature anomalies of the Mediterranean (MED), Tropical North Atlantic (TNA), equatorial Pacific (El Niño Southern Oscillation) and the North Atlantic Oscillation (NAO) index were found robustly correlated with annual forage yield values. Relying only on climatic predictors, we developed a stepwise statistical multi-regression model with leave-one-out cross-validation. Under specific management conditions (e.g., three annual cuts) and from one to five months in advance, the generated model successfully provided a p-value<0.01 in correlation (t-test), a root mean square error percentage (%RMSE) of 14.6% and a 71.43% hit rate predicting above/below average years in terms of forage yield collection. This is the first modeling study on the possible role of large-scale oceanic–atmospheric teleconnections in driving forage production in Europe. As such, it provides a useful springboard to implement a grassland seasonal forecasting system in this continent.
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