2017, Käthner, Jana, Ben-Gal, Alon, Gebbers, Robin, Peeters, Aviva, Herppich, Werner B., Zude-Sasse, Manuela

In orchards, the variations of fruit quality and its determinants are crucial for resource effective measures. In the present study, a drip-irrigated plum production (Prunus domestica L. “Tophit plus”/Wavit) located in a semi-humid climate was studied. Analysis of the apparent electrical conductivity (ECa) of soil showed spatial patterns of sand lenses in the orchard. Water status of sample trees was measured instantaneously by means of leaf water potential, Ψleaf [MPa], and for all trees by thermal imaging of canopies and calculation of the crop water stress index (CWSI). Methods for determining CWSI were evaluated. A CWSI approach calculating canopy and reference temperatures from the histogram of pixels from each image itself was found to suit the experimental conditions. Soil ECa showed no correlation with specific leaf area ratio and cumulative water use efficiency (WUEc) derived from the crop load. The fruit quality, however, was influenced by physiological drought stress in trees with high crop load and, resulting (too) high WUEc, when fruit driven water demand was not met. As indicated by analysis of variance, neither ECa nor the instantaneous CWSI could be used as predictors of fruit quality, while the interaction of CWSI and WUEc did succeed in indicating significant differences. Consequently, both WUEc and CWSI should be integrated in irrigation scheduling for positive impact on fruit quality.

2015, Waha, Katharina, Huth, Neil, Carberry, Peter, Wang, Enli

Crop models are common tools for simulating crop yields and crop production in studies on food security and global change. Various uncertainties however exist, not only in the model design and model parameters, but also and maybe even more important in soil, climate and management input data. We analyze the performance of the point-scale crop model APSIM and the global scale crop model LPJmL with different climate and soil conditions under different agricultural management in the low-input maize-growing areas of Burkina Faso, West Africa. We test the models' response to different levels of input information from little to detailed information on soil, climate (1961–2000) and agricultural management and compare the models' ability to represent the observed spatial (between locations) and temporal variability (between years) in crop yields. We found that the resolution of different soil, climate and management information influences the simulated crop yields in both models. However, the difference between models is larger than between input data and larger between simulations with different climate and management information than between simulations with different soil information. The observed spatial variability can be represented well from both models even with little information on soils and management but APSIM simulates a higher variation between single locations than LPJmL. The agreement of simulated and observed temporal variability is lower due to non-climatic factors e.g. investment in agricultural research and development between 1987 and 1991 in Burkina Faso which resulted in a doubling of maize yields. The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance.