Cover crops provide many ecosystem services, such as soil protection, nitrate pollution of water mitigation, and green manure effects. However, the impact of cover crops on soil water balance is poorly studied, despite its potential impact on groundwater recharge. Some studies reported a reduction of the water drainage due to an increase of the evapotranspiration by plant cover transpiration. However, there is no real consensus on the intensity of this phenomenon, which highlights the importance to quantify the impact of cover crops on drainage compared to that of bare soil. We performed a meta-analysis of published papers presenting studies on the impact of cover crops on drainage compared to that of bare soil under temperate climates. Of the 436 papers identified, 28 of them were included in the analysis based on criteria required for performing a relevant meta-analysis. The originality of our study lies in two following results: (1) the quantification of drainage reduction with cover crops by a mean effect size of 27 mm compared to that of bare soil and (2) within the large variability of soils, climates, and cropping systems, no main determining factor was found significant to explain the variability of water drainage reduction. The cover crops provide a service of nitrate pollution mitigation, but the drainage reduction could be considered as a disservice, because they can lead to a reduction in groundwater recharge due to a higher evapotranspiration in comparison to bare soil. This highlights the need of research for optimizing trade-offs between services and disservices of cover crops for water balance.
The adjustment of the parameters in mechanistic crop models to field data, using an automatic procedure, is essential to ensure efficient and objective use of measured data. However, it is in general numerically impossible, and in any case undoubtedly unwise, to adjust all the model parameters to the measured data. There is currently no widely accepted solution to this problem. This paper proposes a new approach to parameter adjustment, and applies it to a model of corn growth and development. One begins by defining a criterion of model goodness‐of‐fit, which should be adapted to the goal of the modeling exercise, and a corresponding criterion of model prediction error. For the latter we propose a cross validation version of the goodness‐of‐fit criterion. In Step 1 of the algorithm, one orders the parameters according to how much each improves the goodness‐of‐fit of the model. In the second step, the number of parameters actually adjusted is chosen to minimize the prediction error criterion. This approach has the advantage of explicitly using prediction quality as a criterion. As a by‐product, it leads to adjusting relatively few parameters (in our example, 3 out of the 26 potentially adjustable parameters), which considerably reduces the numerical problems. The procedure is quite straightforward to apply, although it does require substantial computing time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.