Implementation and calibration of the parameter-sparse Yield-SAFE model to predict production and land equivalent ratio in mixed tree and crop systems under two contrasting production situations in Europe

Modeling Pinus radiata D. Don growth and pasture production under different land uses and climate scenarios

Yield-SAFE is a biophysical model to predict long-term production according to light and water availability in agricultural, forest, and agroforestry systems. The Yield-SAFE model should be calibrated and validated for the highest number of tree species and crops to be used as a management tool that takes into account climate change. This study aimed to calibrate and validate the Yield-SAFE model for Pinus radiata D. Don and sown pasture (Dactylis glomerata L.) to estimate the production in (1) forest systems, (2) agricultural systems, and (3) silvopastoral systems established in Galicia (NW Spain) under different conditions of climate: (i) reference “current” climate from 1961 to 1990, (ii) climate from 2021 to 2050, and (iii) climate from 2051 to 2080. The Yield-SAFE model can now be used to assess the long-term productivity of P. radiata D. Don and D. glomerata L. under different land uses and climate conditions. The Yield-SAFE model simulated similar tree and pasture growth in all scenarios of climate because the inter-annual variation of climate was small. However, tree growth estimated with the Yield-SAFE model was higher in the silvopastoral systems than in the forest systems, indicating that land use had more impact on land productivity than climate. Therefore, in regions such as Galicia, the Yield-SAFE model could be used as a tool to support the land use change in an agroforestry context, whilst also including climate scenarios which is considered a valuable solution to mitigate the effect of climate change.

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI) of barley at three different conventionally managed silvoarable alley cropping agroforestry sites located in Germany. DM and LAI were modelled using random forest regression models with good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: R² 0.92, nRMSEp 7.1%). Important variables for prediction included normalized reflectance, vegetation indices, texture and plant height. Maps were produced from model predictions for spatial analysis, showing significant effects of distance to trees on DM and LAI. Spatial patterns differed greatly between the sampled sites and suggested management and soil effects overriding tree effects across large portions of 96 m wide crop alleys, thus questioning alleged impacts of AFS tree rows on yield distribution in intensively managed barley populations. Models based on UAV-borne imagery proved to be a valuable novel tool for prediction of DM and LAI at high accuracies, revealing spatial variability in AFS with high spatial resolution and coverage.

Crop Yields in European Agroforestry Systems: A Meta-Analysis

Agroforestry is the practice of integrating woody vegetation with crops and/or livestock production in order to strengthen ecological services on farmland and achieve a more multi-functional agricultural system. Crop yields determine economic outcomes when trees are young, but information on yields is scattered in the literature and a quantitative overview of crop yields in European agroforestry systems is lacking. We therefore synthesized published information on crop yields in European agroforestry systems, using meta-analysis. A systematic review of the literature was conducted, highlighting quantitative information on yields is available only for traditional Dehesa systems in Spain and Portugal and for modern alley cropping experiments, mostly in northern Europe. Relative cereal crop yields in alley cropping systems (systems with tree rows with interspersed crop strips) were 96% of sole crop yield at tree planting. Crop yields in alley cropping decreased on average with 2.6% per year over the first 21 years of the tree stand, indicating increasing competitive effects of the trees with their age. On the other hand, studies in traditional Dehesa and Montado systems in Southern Europe showed no negative influence of the trees on crop production, indicating that competition between crops and trees plays a less important role in those systems than in alley cropping. Overall, the systematic review showed a need for more experimental data to further substantiate the benefits of agroforestry and elucidate optimal agroforestry practices under European conditions.

Handling Data Gaps in Reported Field Measurements of Short Rotation Forestry

Filling missing data in forest research is paramount for the analysis of primary data, forest statistics, land use strategies, as well as for the calibration/validation of forest growth models. Consequently, our main objective was to investigate several methods of filling missing data under a reduced sample size. From a complete dataset containing yearly first-rotation tree growth measurements over a period of eight years, we gradually retrieved two and then four years of measurements, hence operating on 72% and 43% of the original data. Secondly, 15 statistical models, five forest growth functions, and one biophysical, process-oriented, tree growth model were employed for filling these data gap representations accounting for 72% and 43% of the available data. Several models belonging to (i) regression analysis, (ii) statistical imputation, (iii) forest growth functions, and (iv) tree growth models were applied in order to retrieve information about the trees from existing yearly measurements. Subsequently, the findings of this study could lead to finding a handy tool for both researchers and practitioners dealing with incomplete datasets. Moreover, we underline the paramount demand for far-sighted, long-term research projects for the expansion and maintenance of a short rotation forestry (SRF) repository.

Hi-sAFe: A 3D Agroforestry Model for Integrating Dynamic Tree–Crop Interactions

Agroforestry, the intentional integration of trees with crops and/or livestock, can lead to multiple economic and ecological benefits compared to trees and crops/livestock grown separately. Field experimentation has been the primary approach to understanding the tree–crop interactions inherent in agroforestry. However, the number of field experiments has been limited by slow tree maturation and difficulty in obtaining consistent funding. Models have the potential to overcome these hurdles and rapidly advance understanding of agroforestry systems. Hi-sAFe is a mechanistic, biophysical model designed to explore the interactions within agroforestry systems that mix trees with crops. The model couples the pre-existing STICS crop model to a new tree model that includes several plasticity mechanisms responsive to tree–tree and tree–crop competition for light, water, and nitrogen. Monoculture crop and tree systems can also be simulated, enabling calculation of the land equivalent ratio. The model’s 3D and spatially explicit form is key for accurately representing many competition and facilitation processes. Hi-sAFe is a novel tool for exploring agroforestry designs (e.g., tree spacing, crop type, tree row orientation), management strategies (e.g., thinning, branch pruning, root pruning, fertilization, irrigation), and responses to environmental variation (e.g., latitude, climate change, soil depth, soil structure and fertility, fluctuating water table). By improving our understanding of the complex interactions within agroforestry systems, Hi-sAFe can ultimately facilitate adoption of agroforestry as a sustainable land-use practice.

Simulating Climate Change Impacts on Hybrid-Poplar and Black Locust Short Rotation Coppices

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.