We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, ...continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.
The global challenges of food security and biodiversity are rarely addressed together, though recently there has been an increasing awareness that the two issues are closely related. The majority of ...land available for agriculture is already used for food production, but despite the productivity gains, one in nine people worldwide are classified as food insecure. There is an increasing risk that addressing food insecurity through methods such as agricultural expansion or intensification could lead to biodiversity loss through destruction of habitats important for conservation. This analysis uses various indicators of biodiversity at a global scale, including biodiversity hotspots, total species richness, and threatened and endemic species richness. Areas where high biodiversity coexists with high food insecurity or a high risk of agricultural expansion, were examined and found to mainly occur in the tropics, with Madagascar standing out in particular. The areas identified are especially at risk of biodiversity loss, and so are global priorities for further research and for policy development to address food insecurity and biodiversity loss together.
Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is ...the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux‐related performance of a set of crop models. The aim was to predict weighing lysimeter‐based crop (i.e., agronomic) and water‐related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014–2018) were from the Dedelow (Dd), Bad Lauchstädt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop‐ and soil‐related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi‐model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site‐specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil‐related data (i.e., water fluxes and system states) when simulating soil–crop–climate interrelations in changing climatic conditions.
Core Ideas
We demonstrate the use of high precision weighable lysimeter for full model calibration and validation.
Lysimeter data from translocated soils represent effects of changing climatic conditions.
We compare calibration with blind forward simulations (fixed soil and calibrated crop parameter).
We compare individual crop model predictions with multi‐model mean.
We test the predictive ability of crop models and multi‐model mean.
Agroecosystem models need to reliably simulate all biophysical processes that control crop growth, particularly the soil water fluxes and nutrient dynamics. As a result of the erosion history, ...truncated and colluvial soil profiles coexist in arable fields. The erosion‐affected field‐scale soil spatial heterogeneity may limit agroecosystem model predictions. The objective was to identify the variation in the importance of soil properties and soil profile modifications in agroecosystem models for both agronomic and environmental performance. Four lysimeters with different soil types were used that cover the range of soil variability in an erosion‐affected hummocky agricultural landscape. Twelve models were calibrated on crop phenological stages, and model performance was tested against observed grain yield, aboveground biomass, leaf area index, actual evapotranspiration, drainage, and soil water content. Despite considering identical input data, the predictive capability among models was highly diverse. Neither a single crop model nor the multi‐model mean was able to capture the observed differences between the four soil profiles in agronomic and environmental variables. The model's sensitivity to soil‐related parameters was apparently limited and dependent on model structure and parameterization. Information on phenology alone seemed insufficient to calibrate crop models. The results demonstrated model‐specific differences in the impact of soil variability and suggested that soil matters in predictive agroecosystem models. Soil processes need to receive greater attention in field‐scale agroecosystem modeling; high‐precision weighable lysimeters can provide valuable data for improving the description of soil–vegetation–atmosphere process in the tested models.
Rice (Oryza sativa L.) is cultivated as a major crop in most Asian countries and its production is expected to increase to meet the demands of a growing population. This is expected to increase ...greenhouse gas (GHG) emissions from paddy rice ecosystems, unless mitigation measures are in place. It is therefore important to assess GHG mitigation potential whilst maintaining yield. Using the process-based ecosystem model DayCent, a spatial analysis was carried out in a rice harvested area in Bangladesh for the period 1996 to 2015, considering the impacts on soil organic carbon (SOC) sequestration, GHG emissions and yield under various mitigation options. An integrated management (IM, a best management practice) considering reduced water, tillage with residue management, reduced mineral nitrogen fertilizer and manure, led to a net offset by, on average, −2.43 t carbon dioxide equivalent (CO2-eq.) ha−1 year−1 (GHG removal) and a reduction in yield-scaled emissions intensity by −0.55 to −0.65 t CO2-eq. t−1 yield. Under integrated management, it is possible to increase SOC stocks on average by 1.7% per year in rice paddies in Bangladesh, which is nearly 4 times the rate of change targeted by the “4 per mille” initiative arising from the Paris Climate Agreement.
Microtopography and roughness are highly dynamic properties of the soil surface and important factors governing surface runoff and erosion processes. While various remote sensing technologies were ...successfully applied for topography measurements at different spatial scales, there is a lack of field studies that collected systematically microtopography data over long observation periods. In this paper an approach to measure and quantify surface roughness in the field based on laser scanning technologies is presented. Between June 2004 and November 2005 97 in-situ measurements were conducted in a test site with two different sandy substrates in vegetation-free conditions. Two-dimensional high-resolution (1 mm) datasets where generated for eight micro erosion plots of 0.25 to 2.9 m
2 in size. Dynamics and pattern formation were quantified for surface roughness and surface height changes. Roughness patterns at different scales were analyzed by local roughness indices using sliding windows of 3 to 55 mm in size. Results show strong spatial and temporal dynamics in surface roughness as well as substrate-specific variations. Temporal roughness variations could be detected and were linked to precipitation patterns. The methods presented in this paper are considered suitable to generate high-resolution datasets on spatiotemporal and multi-scale microtopography patterns and to advance the understanding of surface processes at small scales in natural environments.
Matthias Kuhnert geht der Frage nach, wie zivilgesellschaftliche Gruppen bei der Bevölkerung um Unterstützung für ihre Tätigkeit warben. Am Beispiel zweier britischer NGOs, War on Want und Christian ...Aid, wird deutlich, welche Emotionen humanitäre Organisationen einsetzten, um ihre Botschaften zu vermitteln und Hilfsbereitschaft zu generieren. Durch den Vergleich christlicher und linker Organisationen kann der Autor zeigen, dass sich mit dem Wandel des Humanitarismus in der Nachkriegszeit nicht nur die Art und Weise humanitären und entwicklungspolitischen Engagements, sondern auch die emotionale Dimension humanitären Handelns veränderte. Zum ersten Mal liegt nun eine Untersuchung über die Transformation humanitären Engagements von der Nachkriegszeit bis Anfang der 1990er Jahre vor, die emotions- und wissensgeschichtliche Ansätze verbindet.
Nitrous oxide emission factors (N2O-EF, percentage of N2O–N emissions arising from applied fertilizer N) for cropland emission inventories can vary with agricultural management, soil properties and ...climate conditions. Establishing a regionally-specific EF usually requires the measurement of a whole year of N2O emissions, whereas most studies measure N2O emissions only during the crop growing season, neglecting emissions during non-growing periods. However, the difference in N2O-EF (ΔEF) estimated using measurements over a whole year (EFwy) and those based on measurement only during the crop-growing season (EFgs) has received little attention. Here, we selected 21 studies including both the whole-year and growing-season N2O emissions under control and fertilizer treatments, to obtain 123 ΔEFs from various agroecosystems globally. Using these data, we conducted a meta-analysis of the ΔEFs by bootstrapping resampling to assess the magnitude of differences in response to management-related and environmental factors. The results revealed that, as expected, the EFwy was significantly greater than the EFgs for most crop types. Vegetables showed the largest ΔEF (0.19%) among all crops (0.07%), followed by paddy rice (0.11%). A higher ΔEF was also identified in areas with rainfall ≥600 mm yr−1, soil with organic carbon ≥1.3% and acidic soils. Moreover, fertilizer type, residue management, irrigation regime and duration of the non-growing season were other crucial factors controlling the magnitude of the ΔEFs. We also found that neglecting emissions from the non-growing season may underestimate the N2O-EF by 30% for paddy fields, almost three times that for non-vegetable upland crops. This study highlights the importance of the inclusion of the non-growing season in the measurements of N2O fluxes, the compilation of national inventories and the design of mitigation strategies.
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•Whole year N2O emission factors (EFs) were generally greater than growing season EFs.•Excluding difference between whole year and growing season lowered the EF by 30% for paddy rice.•Excluding the difference lowered the EF by 10% for non-vegetable crops.•Higher difference found in areas with rainfall ≥600 mm yr−1, SOC ≥1.3% and pH < 7.
Fallow-season N2O emissions must be included when calculating emission factors (EFs); neglecting them lowers the EFs by 30% for paddy rice and 10% for non-vegetable crops.
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