Publications by authors named "Iwan Supit"

12 Publications

  • Page 1 of 1

Modeling the Contribution of Crops to Nitrogen Pollution in the Yangtze River.

Environ Sci Technol 2020 10 11;54(19):11929-11939. Epub 2020 Sep 11.

College of Resources and Environment, Southwest University, Tiansheng Road 02, Chongqing 400715, China.

Agriculture contributes considerably to nitrogen (N) inputs to the world's rivers. In this study, we aim to improve our understanding of the contribution of different crops to N inputs to rivers. To this end, we developed a new model system by linking the MARINA 2.0 (Model to Assess River Input of Nutrient to seAs) and WOFOST (WOrld FOod STudy) models. We applied this linked model system to the Yangtze as an illustrative example. The N inputs to crops in the Yangtze River basin showed large spatial variability. Our results indicate that approximately 6,000 Gg of N entered all rivers of the Yangtze basin from crop production as dissolved inorganic N (DIN) in 2012. Half of this amount is from the production of single rice, wheat, and vegetables, where synthetic fertilizers were largely applied. In general, animal manure contributes 12% to total DIN inputs to rivers. Three-quarters of manure-related DIN in rivers are from vegetable, fruit, and potato production. The contributions of crops to river pollution differ among sub-basins. For example, potato is an important source of DIN in rivers of some upstream sub-basins. Our results may help to prioritize the dominant crop sources for management to mitigate N pollution in the future.
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http://dx.doi.org/10.1021/acs.est.0c01333DOI Listing
October 2020

Climate change impact and adaptation for wheat protein.

Glob Chang Biol 2019 01 22;25(1):155-173. Epub 2018 Nov 22.

Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany.

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 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low-rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by -1.1 percentage points, representing a relative change of -8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.
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http://dx.doi.org/10.1111/gcb.14481DOI Listing
January 2019

Global wheat production with 1.5 and 2.0°C above pre-industrial warming.

Glob Chang Biol 2018 Dec 7. Epub 2018 Dec 7.

Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany.

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 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.
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http://dx.doi.org/10.1111/gcb.14542DOI Listing
December 2018

The Mekong's future flows under multiple drivers: How climate change, hydropower developments and irrigation expansions drive hydrological changes.

Sci Total Environ 2019 Feb 17;649:601-609. Epub 2018 Aug 17.

Water Systems and Global Change Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands.

The river flow regime and water resources are highly important for economic growths, flood security, and ecosystem dynamics in the Mekong basin - an important transboundary river basin in South East Asia. The river flow, although remains relatively unregulated, is expected to be increasingly perturbed by climate change and rapidly accelerating socioeconomic developments. Current understanding about hydrological changes under the combined impacts of these drivers, however, remains limited. This study presents projected hydrological changes caused by multiple drivers, namely climate change, large-scale hydropower developments, and irrigated land expansions by 2050s. We found that the future flow regime is highly susceptible to all considered drivers, shown by substantial changes in both annual and seasonal flow distribution. While hydropower developments exhibit limited impacts on annual total flows, climate change and irrigation expansions cause changes of +15% and -3% in annual flows, respectively. However, hydropower developments show the largest seasonal impacts characterized by higher dry season flows (up to +70%) and lower wet season flows (-15%). These strong seasonal impacts tend to outplay those of the other drivers, resulting in the overall hydrological change pattern of strong increases of the dry season flow (up to +160%); flow reduction in the first half of the wet season (up to -25%); and slight flow increase in the second half of the wet season (up to 40%). Furthermore, the cumulative impacts of all drivers cause substantial flow reductions during the early wet season (up to -25% in July), posing challenges for crop production and saltwater intrusion in the downstream Mekong Delta. Substantial flow changes and their consequences require careful considerations of future development activities, as well as timely adaptation to future changes.
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http://dx.doi.org/10.1016/j.scitotenv.2018.08.160DOI Listing
February 2019

Multimodel ensembles improve predictions of crop-environment-management interactions.

Glob Chang Biol 2018 11 24;24(11):5072-5083. Epub 2018 Aug 24.

Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK.

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.
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http://dx.doi.org/10.1111/gcb.14411DOI Listing
November 2018

The uncertainty of crop yield projections is reduced by improved temperature response functions.

Nat Plants 2017 07 17;3:17102. Epub 2017 Jul 17.

Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany.

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.
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http://dx.doi.org/10.1038/nplants.2017.102DOI Listing
July 2017

A potato model intercomparison across varying climates and productivity levels.

Glob Chang Biol 2017 03 20;23(3):1258-1281. Epub 2016 Oct 20.

AgWeatherNet Program, Washington State University, Pullman, WA, USA.

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.
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http://dx.doi.org/10.1111/gcb.13411DOI Listing
March 2017

Climate change and maize yield in southern Africa: what can farm management do?

Glob Chang Biol 2015 Dec;21(12):4588-601

Plant Production Systems, Wageningen University, PO Box 430, 6700AK Wageningen, The Netherlands.

There is concern that food insecurity will increase in southern Africa due to climate change. We quantified the response of maize yield to projected climate change and to three key management options - planting date, fertilizer use and cultivar choice - using the crop simulation model, agricultural production systems simulator (APSIM), at two contrasting sites in Zimbabwe. Three climate periods up to 2100 were selected to cover both near- and long-term climates. Future climate data under two radiative forcing scenarios were generated from five global circulation models. The temperature is projected to increase significantly in Zimbabwe by 2100 with no significant change in mean annual total rainfall. When planting before mid-December with a high fertilizer rate, the simulated average grain yield for all three maize cultivars declined by 13% for the periods 2010-2039 and 2040-2069 and by 20% for 2070-2099 compared with the baseline climate, under low radiative forcing. Larger declines in yield of up to 32% were predicted for 2070-2099 with high radiative forcing. Despite differences in annual rainfall, similar trends in yield changes were observed for the two sites studied, Hwedza and Makoni. The yield response to delay in planting was nonlinear. Fertilizer increased yield significantly under both baseline and future climates. The response of maize to mineral nitrogen decreased with progressing climate change, implying a decrease in the optimal fertilizer rate in the future. Our results suggest that in the near future, improved crop and soil fertility management will remain important for enhanced maize yield. Towards the end of the 21st century, however, none of the farm management options tested in the study can avoid large yield losses in southern Africa due to climate change. There is a need to transform the current cropping systems of southern Africa to offset the negative impacts of climate change.
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http://dx.doi.org/10.1111/gcb.13061DOI Listing
December 2015

Multimodel ensembles of wheat growth: many models are better than one.

Glob Chang Biol 2015 Feb 3;21(2):911-25. Epub 2014 Dec 3.

INRA, UMR1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC), 5 chemin de Beaulieu, F-63 100, Clermont-Ferrand, France; Blaise Pascal University, UMR1095 GDEC, F-63 170, Aubière, France.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
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http://dx.doi.org/10.1111/gcb.12768DOI Listing
February 2015
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