Patterns of cropland management systems for assessments of global change

Die Landwirtschaft liefert einen Großteil der Nahrungsmittel und Rohstoffe für den menschlichen Verbrauch und wird aufgrund globaler Dynamiken des Bevölkerungswachstums, Änderungen der Ernährungszusammensetzung und Auswirkungen des Klimawandels herausgefordert. Gleichzeitig hat die intensive landwirtschaftliche Produktion oft erhebliche Auswirkungen auf die Leistungen und Funktionen von Ökosystemen. Agrarökosystemmodelle können verwendet werden, um Auswirkungen der Landwirtschaft über verschiedene zeitliche und räumliche Skalen zu quantifizieren. Globale Bewertungen werden jedoch durch die begrenzte Verfügbarkeit von Daten einzelner agronomischer Maßnahmen und dem Wissen über die damit verbundenen biophysikalischen und biogeochemischen Prozesse erschwert. Ziel dieser Doktorarbeit ist es, das Verständnis über Anforderungen an Daten von landwirtschaftlichen Produktionssystemen und deren Anwendungsmethoden in globalen Modellierungsstudien zu erweitern. Darüber hinaus zielt diese Doktorarbeit darauf ab die Arten, räumliche Ausdehnung, Umweltwirkung und Potenziale von unterschiedlichen Bewirtschaftungsmethoden auf globalem Ackerland abzuschätzen. Die Ergebnisse der ersten Studie zeigen, inwiefern die Aggregation von rasterzellenbasiert simulierten Ernteerträgen zu national und globalen Durchschnittserträgen mit vier unterschiedlichen Datenprodukten zu Unsicherheiten von ~10 % führen kann. Der zweite Forschungsartikel präsentiert eine Klassifikation von sechs Bodenbearbeitungssystemen, deren Kartierung und ermittelten Merkmale zur Parametrisierung in globalen Agrarökosystemmodell verwendet werden können. Zuletzt werden mit Hilfe des globalem Modells LPJml5.0- tillage-cc die biophysikalischen Auswirkungen von Zwischenfruchtanbau im Vergleich zu Schwarzbrache auf die Kohlenstoff-, Stickstoff- und Wasserdynamiken abgeschätzt. Die Ergebnisse der Thesis zeigen Potenziale von und Trade-offs zwischen ackerbaulichen Bewirtschaftungsmethoden und deren globaler Modellierung auf.Agricultural production provides a major share of food, feed, fiber, and fuel for anthropogenic usage, and is challenged by projected increasing demand due to dynamics of population growth, changes in dietary compositions, and climate change impacts. At the same time, intensive agricultural production practices have environmental externalities, which negatively affect ecosystems’ services and functions. Agroecosystem models can be used to quantify impacts of cropland use across various temporal and spatial scales, but global assessments are hampered by the limited availability of land management data and of knowledge regarding associated biophysical and biogeochemical processes and functions. The objective of the thesis is to increase the understanding of agricultural management data requirements and implications for their usages in global modeling studies. Further, the thesis aims to identify types, spatial distribution, as well as to estimate impacts, and potentials of cropland management practices to support sustainable development. In the first study, it was assessed in which way the application of different harvested crop area datasets for the national and global aggregation of modeled crop yield outputs from the grid cell to country and global scale, induces average uncertainty of ~10 % to the results. The second study presents a global classification of six soil management systems whereas the derived mapping and characteristics can be used for parameterization across a range of intensity levels in global land use modeling studies. In the third study different cropland management practices were assessed using LPJml5.0-tillage-cc, with a modified code for the representation of cover crops growing as grass on cropland between two consecutive main crop growing seasons. The thesis’ findings reveal potential and trade-offs of land management practices and their impact assessment using global agroecosystem models

Generating a rule-based global gridded tillage dataset

Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models, but global assessments are hampered by lack of information on the type of tillage and their spatial distribution. This study describes the generation of a classification of tillage practices and presents the spatially explicit mapping of these crop-specific tillage systems for around the year 2005. Tillage practices differ by the kind of equipment used, soil surface and depth affected, timing, and their purpose within the cropping systems. We classified the broad variety of globally relevant tillage practices into six categories: no-tillage in the context of Conservation Agriculture, traditional annual, traditional rotational, rotational, reduced, and conventional annual tillage. The identified tillage systems were allocated to gridded crop-specific cropland areas with a resolution of 5 arcmin. Allocation rules were based on literature findings and combine area information on crop type, water management regime, field size, water erosion, income, and aridity. We scaled reported national Conservation Agriculture areas down to grid cells via a probability-based approach for 54 countries. We provide area estimates of the six tillage systems aggregated to global and country scale. We found that 8.67Mkm2 of global cropland area was tilled intensively at least once a year, whereas the remaining 2.65Mkm2 was tilled less intensely. Further, we identified 4.67Mkm2 of cropland as an area where Conservation Agriculture could be expanded to under current conditions. The tillage classification enables the parameterization of different soil management practices in various kinds of model simulations. The crop-specific tillage dataset indicates the spatial distribution of soil management practices, which is a prerequisite to assess erosion, carbon sequestration potential, as well as water, and nutrient dynamics of cropland soils. The dynamic definition of the allocation rules and accounting for national statistics, such as the share of Conservation Agriculture per country, also allow for derivation of datasets for historical and future global soil management scenarios. The resulting tillage system dataset and source code are accessible via an open-data repository (DOIs: https://doi.org/10.5880/PIK.2019.009 and https://doi.org/10.5880/PIK.2019.010, Porwollik et al., 2019a, b). © Author(s) 2019

Modelling approach and first results on irrigation as climate change adaptation strategy of the project NaLaMa-nT

The project NaLaMa-nT examines in the context of climate change sustainable development paths of land use in four different rural districts in Northern Germany. These districts were chosen along a soil-climate gradient from west to east with increasing water deficit for plant growth caused by both: decreasing rain fall and decreasing soil quality. In front of this background different trends and developments of agricultural production can be derived from analysing, modelling and comparing existing production systems and conditions of the different regions. One assumption developed from existing climate projections is that climate change will cause increasing water deficits for plant growth – especially in the eastern part of Germany. An obvious solution is to intensify agricultural production using existing irrigation methods that can reduce the yield risk and thus stabilize income from agriculture by avoiding yield failures and increasing the overall yield level. Therefore we build a modelling approach which allows an economic analysis both on the crop production activity level as well on the farm level. The data base comprises data representing recent production techniques and added optional irrigation techniques. The yields and input level changes are derived from literature studies and expert interviews. The farm structure is represented and modeled based on typical farms chosen from an IACS-data farm typology with different production potentials and patterns. First results will be presented in April

PROFITABILITY OF IRRIGATION UNDER THE EFFECTS OF CLIMATE CHANGE – A SITE AND CROP SPECIFIC ASSESSMENT AT THE EXAMPLE OF BRANDENBURG

Irrigation is seen as an appropriate adaptation measure to the effects of climate change. However, the costs of irrigation are not always covered by the additional revenue. Based on simulated yields using the crop growth model HERMES for different climate scenarios we estimate the profitability for three typical agricultural crops for different soil quality levels in the federal state of Brandenburg. The results show that not in all cases irrigation can be profitably applied. Medium quality soils are in general the sites that turn irrigation into a profitable revenue. Future crop price increases could turn irrigation more profitable, but the increasing irrigation water demands need to be met by water availability which is already a concern in some regions of Germany

Spatial and temporal uncertainty of crop yield aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r. =0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r. =0.56 (maize, India), r. =0.05 (wheat, Russia), r. =0.13 (rice, Vietnam), and r. =-0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises

Spatial and temporal uncertainty of crop yield aggregations

International audienceThe aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05 (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises