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.

Abstract: This study evaluates the effect of planting three cover crops (CCs) (barley, Hordeum vulgare L.; vetch, Vicia villosa L.; rape, Brassica napus L.) on the direct emission of N(2)O, CO(2) and CH(4) in the intercrop period and the impact of incorporating these CCs on the emission of greenhouse gas (GHG) from the forthcoming irrigated maize (Zea mays L.) crop. Vetch and barley were the CCs with the highest N(2)O and CO(2) losses (75 and 47% increase compared with the control, respectively) in the fallow period. In all cases, fluxes of N(2)O were increased through N fertilization and the incorporation of barley and rape residues (40 and 17% increase, respectively). The combination of a high C:N ratio with the addition of an external source of mineral N increased the fluxes of N(2)O compared with -Ba and -Rp. The direct emissions of N(2)O were lower than expected for a fertilized crop (0.10% emission factor, EF) compared with other studies and the IPCC EF. These results are believed to be associated with a decreased NO(3)(-) pool due to highly denitrifying conditions and increased drainage. The fluxes of CO(2) were in the range of other fertilized crops (i.e., 1118.71-1736.52 kg CO(2)-Cha(-1)). The incorporation of CC residues enhanced soil respiration in the range of 21-28% for barley and rape although no significant differences between treatments were detected. Negative CH(4) fluxes were measured and displayed an overall sink effect for all incorporated CC (mean values of -0.12 and -0.10 kg CH(4)-Cha(-1) for plots with and without incorporated CCs, respectively).

Abstract: ARMOSA is a dynamic simulation model which was developed to simulate crop growth and development, water and nitrogen dynamics under different pedoclimatic conditions and cropping systems in the arable land. The model is meant to be a tool for the evaluation of the impact of different crop management practices on soil nitrogen and carbon cycles and groundwater nitrate pollution. A large data set collected over three to six years from six monitoring sites in Lombardia plain was used to calibrate and validate the model parameters. Measured meteorological data, soil chemical and physical characterizations, crop-related data of different cropping systems allowed for a proper parameterization. Fit indexes showed the reliability of the model in adequately predicting crop-related variables, such as above ground biomass (RRMSE=11.18, EF=0.94, r=0.97), Leaf Area Index maximum value (RRMSE=8.24, EF=0.37, r=0.72), harvest index (RRMSE=19.4, EF=0.32, r=0.74), and crop N uptake (RRMSE=20.25, EF=0.69, r=0.85). Using two different one-year data set from each monitoring site, the model was calibrated and validated, getting to encouraging results: RRMSE=6.28, EF=0.52, r=0.68 for soil water content at different depths, and RRMSE=34.89, EF=0.59, r=0.75 for soil NO3-N content along soil profile. The simulated N leaching was in full agreement with measured data (RRMSE=26.62, EF=0.88, r=0.98).