Mueller, L., Schindler, U., Shepherd, T. G., Ball, B. C., Smolentseva, E., Hu, C., et al. (2012). A framework for assessing agricultural soil quality on a global scale. Archives of Agronomy and Soil Science, 58(sup1), S76–S82.
Abstract: This paper provides information about a novel approach of rating agricultural soil quality (SQ) and crop yield potentials consistently over a range of spatial scales. The Muencheberg Soil Quality Rating is an indicator-based straightforward overall assessment method of agricultural SQ. It is a framework covering aspects of soil texture, structure, topography and climate which is based on 8 basic indicators and more than 12 hazard indicators. Ratings are performed by visual methods of soil evaluation. A field manual is then used to provide ratings from tables based on indicator thresholds. Finally, overall rating scores are given, ranging from 0 (worst) to 100 (best) to characterise crop yield potentials. The current approach is valid for grassland and cropland. Field tests in several countries confirmed the practicability and reliability of the method. At field scale, soil structure is a crucial, management induced criterion of agricultural SQ. At the global scale, climate controlled hazard indicators of drought risk and soil thermal regime are crucial for SQ and crop yield potentials. Final rating scores are well correlated with crop yields. We conclude that this system could be evolved for ranking and controlling agricultural SQ on a global scale.
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Kraus, D., Weller, S., Klatt, S., Haas, E., Wassmann, R., Kiese, R., et al. (2015). A new LandscapeDNDC biogeochemical module to predict CH4 and N2O emissions from lowland rice and upland cropping systems. Plant Soil, 386(1-2), 125–149.
Abstract: Replacing paddy rice by upland systems such as maize cultivation is an on-going trend in SE Asia caused by increasing water scarcity and higher demand for meat. How such land management changes will feedback on soil C and N cycles and soil greenhouse gas emissions is not well understood at present. A new LandscapeDNDC biogeochemical module was developed that allows the effect of land management changes on soil C and N cycle to be simulated. The new module is applied in combination with further modules simulating microclimate and crop growth and evaluated against observations from field experiments. The model simulations agree well with observed dynamics of CH (4) emissions in paddy rice depending on changes in climatic conditions and agricultural management. Magnitude and peak emission periods of N (2) O from maize cultivation are simulated correctly, though there are still deficits in reproducing day-to-day dynamics. These shortcomings are most likely related to simulated soil hydrology and may only be resolved if LandscapeDNDC is coupled to more complex hydrological models. LandscapeDNDC allows for simulation of changing land management practices in SE Asia. The possibility to couple LandscapeDNDC to more complex hydrological models is a feature needed to better understand related effects on soil-atmosphere-hydrosphere interactions.
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Coucheney, E., Buis, S., Launay, M., Constantin, J., Mary, B., García de Cortázar-Atauri, I., et al. (2015). Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France. Env. Model. Softw., 64, 177–190.
Abstract: Soil-crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. (C) 2014 Elsevier Ltd. All rights reserved.
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Waha, K., Müller, C., Bondeau, A., Dietrich, J. P., Kurukulasuriya, P., Heinke, J., et al. (2013). Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa. Glob. Environ. Change, 23(1), 130–143.
Abstract: Multiple cropping systems provide more harvest security for farmers, allow for crop intensification and furthermore influence ground cover, soil erosion, albedo, soil chemical properties, pest infestation and the carbon sequestration potential. We identify the traditional sequential cropping systems in ten sub-Saharan African countries from a survey dataset of more than 8600 households. We find that at least one sequential cropping system is traditionally used in 35% of all administrative units in the dataset, mainly including maize or groundnuts. We compare six different management scenarios and test their susceptibility as adaptation measure to climate change using the dynamic global vegetation model for managed land LPJmL. Aggregated mean crop yields in sub-Saharan Africa decrease by 6-24% due to climate change depending on the climate scenario and the management strategy. As an exception, some traditional sequential cropping systems in Kenya and South Africa gain by at least 25%. The crop yield decrease is typically weakest in sequential cropping systems and if farmers adapt the sowing date to changing climatic conditions. Crop calorific yields in single cropping systems only reach 40-55% of crop calorific yields obtained in sequential cropping systems at the end of the 21st century. The farmers’ choice of adequate crops, cropping systems and sowing dates can be an important adaptation strategy to climate change and these management options should be considered in climate change impact studies on agriculture. (C) 2012 Elsevier Ltd. All rights reserved.
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Müller, C. (2013). African lessons on climate change risks for agriculture. Ann. Rev. Nutr., 33(1), 395–411.
Abstract: Climate change impact assessments on agriculture are subject to large uncertainties, as demonstrated in the present review of recent studies for Africa. There are multiple reasons for differences in projections, including uncertainties in greenhouse gas emissions and patterns of climate change; assumptions on future management, aggregation, and spatial extent; and methodological differences. Still, all projections agree that climate change poses a significant risk to African agriculture. Most projections also see the possibility of increasing agricultural production under climate change, especially if suitable adaptation measures are assumed. Climate change is not the only projected pressure on African agriculture, which struggles to meet demand today and may need to feed an additional one billion individuals by 2050. Development strategies are urgently needed, but they will need to consider future climate change and its inherent uncertainties. Science needs to show how existing synergies between climate change adaptation and development can be exploited.
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