ORCID Profile
0000-0002-8631-8639
Current Organisations
CSIRO
,
University of Augsburg
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Publisher: Elsevier BV
Date: 10-2015
Publisher: Elsevier BV
Date: 02-2017
Publisher: Elsevier BV
Date: 07-2013
Publisher: Springer Science and Business Media LLC
Date: 02-2022
Publisher: Copernicus GmbH
Date: 12-04-2018
Abstract: Abstract. The dynamic global vegetation model LPJmL4 is a process-based model that simulates climate and land use change impacts on the terrestrial biosphere, agricultural production, and the water and carbon cycle. Different versions of the model have been developed and applied to evaluate the role of natural and managed ecosystems in the Earth system and the potential impacts of global environmental change. A comprehensive model description of the new model version, LPJmL4, is provided in a companion paper (Schaphoff et al., 2018c). Here, we provide a full picture of the model performance, going beyond standard benchmark procedures and give hints on the strengths and shortcomings of the model to identify the need for further model improvement. Specifically, we evaluate LPJmL4 against various datasets from in situ measurement sites, satellite observations, and agricultural yield statistics. We apply a range of metrics to evaluate the quality of the model to simulate stocks and flows of carbon and water in natural and managed ecosystems at different temporal and spatial scales. We show that an advanced phenology scheme improves the simulation of seasonal fluctuations in the atmospheric CO2 concentration, while the permafrost scheme improves estimates of carbon stocks. The full LPJmL4 code including the new developments will be supplied open source through gitlab.pik-potsdam.de/lpjml/LPJmL. We hope that this will lead to new model developments and applications that improve the model performance and possibly build up a new understanding of the terrestrial biosphere.
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-13736
Abstract: & & Enhancing and maintaining on-farm ersity represent a potential strategy to improve farming systems sustainability, by reducing the pressure on the natural environment, alleviating farmers' risks and vulnerabilities, and increasing farms resilience. However, farms are complex systems and on-farm ersification, intended as the production of multiple crop, trees and/or livestock species, is not a panacea and it is driven or constrained by different factors and dynamics that vary across environmental, socio-economic and political contexts.& & & & We argue that identifying indicators that reflect these drivers, constraints and contexts at farm scale is crucial to create favourable conditions for the farmers to increase on-farm ersity where doing so is likely to be beneficial. Therefore, the aim of this paper is to identify and clarify some of the patterns behind the process that lead farmers to adopt farm ersification strategies in order to understand where investments and interventions to support ersification are likely to be appropriate and effective, and how they should be targeted.& & & & In this review, we analysed 97 articles, selected from the screening of 2,312 articles retrieved from Web of Science and Scopus, and published in English in peer-reviewed journals since 2010. Our selection criteria required that the articles focused on the analysis of drivers and constraints of agricultural ersification, intended as crops and/or livestock systems, agrobio ersity and agroforestry systems, at farm and household scale.& & & & From the selected studies, we identified and extracted a total of 239 different variables that were statistically assessed as potential drivers and constraints of farm ersity at farm scale. For each of the variables we counted the times they resulted as positive, negative and statistically significant, or not statistically significant. To present and discuss the results, we followed the Sustainable Rural Livelihood Framework, classifying the extracted variables as external (agroecological context, the political and institutional context, and exposure to environmental and market risks and shocks) and internal factors (human, economic/financial, socio-cultural and physical capitals), or other livelihood options (i.e. off-farm income).& & & & Our findings show that the decision to maintain or increase on-farm ersification is a common strategy to cope with environmental and market risks, but that it is often alternative and negatively correlated to the adoption of off-farm livelihood. Overall, the drivers and constraints of ersification were highly context-dependent and contingent. For some relevant variables, such as farm size, household head's age, rainfall level and education, we also found some evidence of the presence of non-linear (e.g. inverted-U) relationships.& & & & These results enforce the hypothesis of the complexity of land uses decision and the importance of understanding farms& #8217 and farmers& #8217 characteristics, and their local and wider context when it comes to design policies and research projects for sustainable rural development.& &
Publisher: Wiley
Date: 25-04-2018
DOI: 10.1111/GCB.14158
Publisher: Wiley
Date: 20-05-2012
Publisher: Elsevier BV
Date: 05-2020
Publisher: Springer Science and Business Media LLC
Date: 22-12-2014
DOI: 10.1038/NCLIMATE2470
Publisher: Annual Reviews
Date: 29-04-2018
DOI: 10.1146/ANNUREV-ARPLANT-042817-040256
Abstract: The eighteenth-century Malthusian prediction of population growth outstripping food production has not yet come to bear. Unprecedented agricultural land expansions since 1700, and technological innovations that began in the 1930s, have enabled more calorie production per capita than was ever available before in history. This remarkable success, however, has come at a great cost. Agriculture is a major cause of global environmental degradation. Malnutrition persists among large sections of the population, and a new epidemic of obesity is on the rise. We review both the successes and failures of the global food system, addressing ongoing debates on pathways to environmental health and food security. To deal with these challenges, a new coordinated research program blending modern breeding with agro-ecological methods is needed. We call on plant biologists to lead this effort and help steer humanity toward a safe operating space for agriculture.
Publisher: Copernicus GmbH
Date: 12-04-2018
Abstract: Abstract. This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates – internally consistently – the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at gitlab.pik-potsdam.de/lpjml/LPJmL, we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process.
Publisher: Inter-Research Science Center
Date: 28-09-2015
DOI: 10.3354/CR01322
Publisher: Elsevier BV
Date: 09-2020
Publisher: Springer Science and Business Media LLC
Date: 11-01-2022
Publisher: Springer Science and Business Media LLC
Date: 12-04-2017
Publisher: Wageningen University and Research
Date: 21-08-2017
DOI: 10.18174/ODJAR.V3I1.15766
Abstract: The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. All data are available via DOI 10.7910/DVN/CJJBSR.
Publisher: IOP Publishing
Date: 04-2020
Abstract: Rangelands are one of the Earth’s major ice-free land cover types. They provide food and support livelihoods for millions of people in addition to delivering important ecosystems services. However, rangelands are at threat from climate change, although the extent and magnitude of the potential impacts are poorly understood. Any declines in vegetation biomass and fluctuations in grazing availability would be of concern for food production and ecosystem integrity and functionality. In this study, we use a global rangeland model in combination with livestock and socio-economic datasets to identify where and to what extent rangeland systems may be at climatic risk. Overall, mean herbaceous biomass is projected to decrease across global rangelands between 2000 and 2050 under RCP 8.5 (−4.7%), while inter- (year-to-year) and intra- (month-to-month) annual variabilities are projected to increase (+21.3% and +8.2%, respectively). These averaged global estimates mask large spatial heterogeneities, with 74% of global rangeland area projected to experience a decline in mean biomass, 64% an increase in inter-annual variability and 54% an increase in intra-annual variability. Half of global rangeland areas are projected to experience simultaneously a decrease in mean biomass and an increase in inter-annual variability—vegetation trends both potentially harmful for livestock production. These regions include notably the Sahel, Australia, Mongolia, China, Uzbekistan and Turkmenistan and support 376 million people and 174 million ruminant Tropical Livestock Units. Additionally, the rangeland communities currently the most vulnerable (here, with the lowest livestock productivities and economic development levels and with the highest projected increases in human population densities) are projected to also experience the most damaging vegetation trends for livestock production. Although the capacity of rangeland systems to adapt is highly complex, analyses such as these generate some of the information required to inform options to facilitate pastoral system mitigation and adaptation strategies under climate change.
Publisher: IOP Publishing
Date: 02-2015
Publisher: Springer Science and Business Media LLC
Date: 09-06-2013
DOI: 10.1038/NCLIMATE1916
Publisher: Elsevier BV
Date: 06-2022
Publisher: Wageningen University and Research
Date: 2015
Publisher: Wiley
Date: 22-11-2018
DOI: 10.1111/GCB.14481
Abstract: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO
Publisher: Wiley
Date: 05-05-2014
DOI: 10.1111/GCB.12586
Abstract: Development efforts for poverty reduction and food security in sub-Saharan Africa will have to consider future climate change impacts. Large uncertainties in climate change impact assessments do not necessarily complicate, but can inform development strategies. The design of development strategies will need to consider the likelihood, strength, and interaction of climate change impacts across biosphere properties. We here explore the spread of climate change impact projections and develop a composite impact measure to identify hotspots of climate change impacts, addressing likelihood and strength of impacts. Overlapping impacts in different biosphere properties (e.g. flooding, yields) will not only claim additional capacity to respond, but will also narrow the options to respond and develop. Regions with severest projected climate change impacts often coincide with regions of high population density and poverty rates. Science and policy need to propose ways of preparing these areas for development under climate change impacts.
Publisher: Elsevier BV
Date: 02-2013
Publisher: Wiley
Date: 26-04-2014
DOI: 10.1111/GCB.12520
Abstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models erge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While in idual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
Publisher: Springer Science and Business Media LLC
Date: 12-09-2016
DOI: 10.1038/NCLIMATE3115
Publisher: Copernicus GmbH
Date: 11-2013
Abstract: Abstract. Dynamic global vegetation models (DGVMs) are important tools for modelling impacts of global change on ecosystem services. However, most models do not take full account of human land management and land use and land cover changes (LULCCs). We integrated croplands and pasture and their management and natural vegetation recovery and succession following cropland abandonment into the LPJ-GUESS DGVM. The revised model was applied to Africa as a case study to investigate the implications of accounting for land use on net ecosystem carbon balance (NECB) and the skill of the model in describing agricultural production and reproducing trends and patterns in vegetation structure and function. The seasonality of modelled monthly fraction of absorbed photosynthetically active radiation (FPAR) was shown to agree well with satellite-inferred normalised difference vegetation index (NDVI). In regions with a large proportion of cropland, the managed land addition improved the FPAR vs. NDVI fit significantly. Modelled 1991–1995 average yields for the seven most important African crops, representing potential optimal yields limited only by climate forcings, were generally higher than reported FAO yields by a factor of 2–6, similar to previous yield gap estimates. Modelled inter-annual yield variations during 1971–2005 generally agreed well with FAO statistics, especially in regions with pronounced climate seasonality. Modelled land–atmosphere carbon fluxes for Africa associated with land use change (0.07 PgC yr−1 release to the atmosphere for the 1980s) agreed well with previous estimates. Cropland management options (residue removal, grass as cover crop) were shown to be important to the land–atmosphere carbon flux for the 20th century.
Publisher: Wiley
Date: 24-01-2019
DOI: 10.1111/GCB.14542
Abstract: Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO
Publisher: Springer Science and Business Media LLC
Date: 17-07-2017
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.
Publisher: Springer Science and Business Media LLC
Date: 03-08-2017
Abstract: This corrects the article DOI: 10.1038/nplants.2017.102.
Publisher: Elsevier BV
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 04-03-2019
Publisher: American Meteorological Society
Date: 10-2011
Abstract: This study compares, spatially explicitly and at global scale, per capita water availability and water requirements for food production presently (1971–2000) and in the future given climate and population change (2070–99). A vegetation and hydrology model Lund–Potsdam–Jena managed Land (LPJmL) was used to calculate green and blue water availability per capita, water requirements to produce a balanced diet representing a benchmark for hunger alleviation [3000 kilocalories per capita per day (1 kilocalorie = 4184 joules), here assumed to consist of 80% vegetal food and 20% animal products], and a new water scarcity indicator that relates the two at country scale. A country was considered water-scarce if its water availability fell below the water requirement for the specified diet, which is presently the case especially in North and East Africa and in southwestern Asia. Under climate (derived from 17 general circulation models) and population change (A2 and B1 emissions and population scenarios), water availability per person will most probably diminish in many regions. At the same time the calorie-specific water requirements tend to decrease, due mainly to the positive effect of rising atmospheric CO2 concentration on crop water productivity—which, however, is very uncertain to be fully realized in most regions. As a net effect of climate, CO2, and population change, water scarcity will become aggravated in many countries, and a number of additional countries are at risk of losing their present capacity to produce a balanced diet for their inhabitants.
Publisher: No publisher found
Date: 2018
DOI: 10.7910/DVN/M9ZT0F
Publisher: Elsevier BV
Date: 11-2015
Publisher: Copernicus GmbH
Date: 12-07-2018
Abstract: Abstract. The well-established dynamical global vegetation, hydrology, and crop growth model LPJmL is extended with a terrestrial nitrogen cycle to account for nutrient limitations. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included in the model. All new model features are described in full detail and the results of a global simulation of the historic past (1901–2009) are presented for evaluation of the model performance. We find that the implementation of nitrogen limitation significantly improves the simulation of global patterns of crop productivity. Regional differences in crop productivity, which had to be calibrated via a scaling of the maximum leaf area index, can now largely be reproduced by the model, except for regions where fertilizer inputs and climate conditions are not the yield-limiting factors. Furthermore, it can be shown that land use has a strong influence on nitrogen losses, increasing leaching by 93 %.
Publisher: Springer Science and Business Media LLC
Date: 06-02-2023
Publisher: Royal Society of Chemistry (RSC)
Date: 1995
DOI: 10.1039/DT9950000191
Publisher: Springer Science and Business Media LLC
Date: 24-05-2016
Abstract: Surveys for more than 9,500 households were conducted in the growing seasons 2002/2003 or 2003/2004 in eleven African countries: Burkina Faso, Cameroon, Ghana, Niger and Senegal in western Africa Egypt in northern Africa Ethiopia and Kenya in eastern Africa South Africa, Zambia and Zimbabwe in southern Africa. Households were chosen randomly in districts that are representative for key agro-climatic zones and farming systems. The data set specifies farming systems characteristics that can help inform about the importance of each system for a country’s agricultural production and its ability to cope with short- and long-term climate changes or extreme weather events. Further it informs about the location of smallholders and vulnerable systems and permits benchmarking agricultural systems characteristics.
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-10304
Abstract: & & Most assessments of the vulnerability of agriculture to climate change do not differentiate between the impacts of climate change on the different cropping systems. However, with the Nile basin dominated by different cropping systems, assessments without examining the influence of climate change on the different cropping systems may bias the understanding of climate change impacts on agriculture. In this study, we use bias corrected climate change data from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and a regionally calibrated SWAT+ model to implement the different cropping systems and assess the impact of climate change on the crop yields from the different cropping systems with in the Nile basin. We assess both a & #8216 no adaptation scenario& #8217 and an & #8216 adaptation scenario to a longer cultivar& #8217 .& & & & Our analyses show that 36.3 % of the crop area in the Nile basin is under multiple (double) cropping. Results show that the combined mean crop yields in the basin decrease by 10.3 & #177 1.3 % with future warming under a & #8216 no adaptation scenario& #8217 but increase by 13.0 & #177 4.3 % under an & #8216 adaptation scenario to a longer cultivar& #8217 . The decrease in mean crop yields under a & #8216 no adaption scenario& #8217 was mainly attributed to the shortening of the maturity period due to increased projected temperature. The decrease signal is stronger in all the single cropping systems (1.3 & #8211 24.6 %) as opposed to the double cropping system (0.3 & #8211 13.3 %) under the no adaption scenario depending on the GCM (General Circulation Model). Likewise, the increase signal is stronger in double cropping systems (9.0 & #8211 19.7 %) compared to the single cropping systems (3.5 & #8211 8.4 %) under the & #8216 adaptation scenario to a longer cultivar& #8217 . Thus, farmers in the Nile basin can possibly benefit from double cropping (higher cropping intensities) systems while reducing the negative impacts of climate change on crop yields. Additionally, adapting to different crop cultivars can potentially abate the impacts of future warming on yields of selected crops.& &
Publisher: Cambridge University Press (CUP)
Date: 18-12-2018
DOI: 10.1017/S0014479718000388
Abstract: Household surveys are one of the most commonly used tools for generating insight into rural communities. Despite their prevalence, few studies comprehensively evaluate the quality of data derived from farm household surveys. We critically evaluated a series of standard reported values and indicators that are captured in multiple farm household surveys, and then quantified their credibility, consistency and, thus, their reliability. Surprisingly, even variables which might be considered ‘easy to estimate’ had instances of non-credible observations. In addition, measurements of maize yields and land owned were found to be less reliable than other stationary variables. This lack of reliability has implications for monitoring food security status, poverty status and the land productivity of households. Despite this rather bleak picture, our analysis also shows that if the same farm households are followed over time, the s le sizes needed to detect substantial changes are in the order of hundreds of surveys, and not in the thousands. Our research highlights the value of targeted and systematised household surveys and the importance of ongoing efforts to improve data quality. Improvements must be based on the foundations of robust survey design, transparency of experimental design and effective training. The quality and usability of such data can be further enhanced by improving coordination between agencies, incorporating mixed modes of data collection and continuing systematic validation programmes.
Publisher: Elsevier BV
Date: 04-2017
Location: Australia
No related grants have been discovered for Katharina Waha.