Intensively managed open croplands are highly productive but often have deleterious environmental impacts. Temperate agroforestry potentially improves ecosystem functions, although comprehensive analysis is lacking. Here, we measured primary data on 47 indicators of seven ecosystem functions in croplands and 16 indicators of four ecosystem functions in grasslands to assess how alley-cropping agroforestry performs compared to open cropland and grassland. Carbon sequestration, habitat for soil biological activity, and wind erosion resistance improved for cropland agroforestry (P ≤ 0.03) whereas only carbon sequestration improved for grassland agroforestry (P < 0.01). In cropland agroforestry, soil nutrient cycling, soil greenhouse gas abatement, and water regulation did not improve, due to customary high fertilization rates. Alley-cropping agroforestry increased multifunctionality, compared to open croplands. To ameliorate the environmental benefits of agroforestry, more efficient use of nutrients is required. Financial incentives should focus on conversion of open croplands to alley-cropping agroforestry and incorporate fertilizer management.

The alley-cropping systems (ACSs), which integrate parallel tree strips at varying distances on an agricultural field can result, complementarity of resource use, in an increased land-use efficiency. Practitioners’ concerns have been directed towards the productivity of such systems given a reduced area covered by agricultural crops. The land equivalent ratio (LER) serves as a valuable productivity indicator of yield performance and land-use efficiency in ACSs, as it compares the yields achieved in monocultures to those from ACSs. Consequently, the objective of this combined experimental and simulation study was to assess the tree- and crop-yields and to derive the LER and gross energy yield for two temperate ACSs in Germany under different design scenarios, i.e., tree arrangements (lee- or wind-ward) and ratios of tree area to crop area. Both LER and gross energy yields resulted in a convex curve where the maximum values were achieved when either the tree or crop component was dominant (>75% of the land area) and minimum when these components shared similar proportions of land area. The implications of several design scenarios have been discussed in order to improve the decision-making, optimization, and adaptation of the design of ACSs with respect to site-specific characteristics.

In Brandenburg, north-eastern Germany, climate change is associated with increasing annual temperatures and decreasing summer precipitation. Appraising short rotation coppices (SRCs), given their long-time planning horizon demands for systematic assessments of woody biomass production under a considerable spectrum of climate change prospects. This paper investigates the prospective growth sensitivity of poplar and black locust SRCs, established in Brandenburg to a variety of weather conditions and long-term climate change, from 2015 to 2054, by a combined experimental and simulation study. The analysis employed (i) a biophysical, process-based model to simulate the daily tree growth and (ii) 100 realisations of the statistical regional climate model STAR 2K. In the last growing period, the simulations showed that the assumed climate change could lead to a decrease in the woody biomass of about 5 Mg ha−1 (18%) for poplar and a decrease of about 1.7 Mg ha−1 (11%) for black locust trees with respect to the median observed in the reference period. The findings corroborate the potential tree growth vulnerability to prospective climatic changes, particularly to changes in water availability and underline the importance of coping management strategies in SRCs for forthcoming risk assessments and adaptation scenarios.

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology as a function of soil, weather, and management is important. Mechanistic crop models are a major tool for such predictions. It has been shown that there is a large variability between predictions by different modeling groups for the same inputs, and therefore, a need for shared improvement of crop models. Two pathways to improvement are through improved understanding of the mechanisms of the modeled system, and through improved model parameterization. This article focuses on improving crop model parameters through improved calibration, specifically for prediction of crop phenology. A detailed calibration protocol is proposed, which covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. For those aspects where knowledge of the model and target environments is required, the protocol gives detailed guidelines rather than strict instructions. The protocol includes documentation tables, to make the calibration process more transparent. The protocol was applied by 19 modeling groups to three data sets for wheat phenology. All groups first calibrated their model using their "usual" calibration approach. Evaluation was based on data from sites and years not represented in the training data. Compared to usual calibration, l calibration following the new protocol significantly reduced the error in predictions for the evaluation data, and reduced the variability between modeling groups by 22%.