| Records |
| Author |
Tao, F.; Zhang, Z.; Zhang, S.; Rötter, R.P.; Shi, W.; Xiao, D.; Liu, Y.; Wang, M.; Liu, F.; Zhang, H. |
| Title |
Historical data provide new insights into response and adaptation of maize production systems to climate change/variability in China |
Type |
Journal Article |
| Year |
2016 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
| Volume |
185 |
Issue |
|
Pages |
1-11 |
Keywords  |
china; climate variability; grain yield; impact; maize; northeast china; tropical maize; wheat yields; heat-stress; crop yields; temperature; impacts; sensitivities; hybrids; trends |
| Abstract |
Extensive studies had been conducted to investigate the impacts of climate change on maize growth and yield in recent decades; however, the dynamics of crop husbandry in response and adaptation to climate change were not taken into account. Based on field observations spanning from 1981 to 2009 at 167 agricultural meteorological stations across China, we found that solar radiation and temperature over the observed maize growth period had decreasing trends during 1981-2009, and maize yields were positively correlated with these climate variables in major production regions. The decreasing trends in solar radiation and temperature during maize growth period were mainly ascribed to the adoption of late maturity cultivars with longer reproductive growth period (RGP). The adoption of late maturing cultivars with longer RGP contributed substantially to grain yield increase during the last three decades. The climate trends during maize growth period varied among different production areas. During 1981-2009, decreases in mean temperature, precipitation and solar radiation over maize growth period jointly reduced yield most by 13.2-17.3% in southwestern China, by contrast in northwestern China increases in mean temperature, precipitation and solar radiation jointly increased yield most by 12.9-14.4%. Our findings highlight that the adaptations of maize production system to climate change through shifts of sowing date and genotypes are underway and should be taken into accounted when evaluating climate change impacts. (C) 2015 Elsevier B.V. All rights reserved. |
| Address |
2016-10-31 |
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| Language |
English |
Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0378-4290 |
ISBN |
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Article |
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Conference |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4816 |
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| Author |
Makowski, D.; Asseng, S.; Ewert, F.; Bassu, S.; Durand, J.L.; Li, T.; Martre, P.; Adam, M.; Aggarwal, P.K.; Angulo, C.; Baron, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Boogaard, H.; Boote, K.J.; Bouman, B.; Bregaglio, S.; Brisson, N.; Buis, S.; Cammarano, D.; Challinor, A.J.; Confalonieri, R.; Conijn, J.G.; Corbeels, M.; Deryng, D.; De Sanctis, G.; Doltra, J.; Fumoto, T.; Gaydon, D.; Gayler, S.; Goldberg, R.; Grant, R.F.; Grassini, P.; Hatfield, J.L.; Hasegawa, T.; Heng, L.; Hoek, S.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Jongschaap, R.E.E.; Jones, J.W.; Kemanian, R.A.; Kersebaum, K.C.; Kim, S.-H.; Lizaso, J.; Marcaida, M.; Müller, C.; Nakagawa, H.; Naresh Kumar, S.; Nendel, C.; O’Leary, G.J.; Olesen, J.E.; Oriol, P.; Osborne, T.M.; Palosuo, T.; Pravia, M.V.; Priesack, E.; Ripoche, D.; Rosenzweig, C.; Ruane, A.C.; Ruget, F.; Sau, F.; Semenov, M.A.; Shcherbak, I.; Singh, B.; Singh, U.; Soo, H.K.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tang, L.; Tao, F.; Teixeira, E.I.; Thorburn, P.; Timlin, D.; Travasso, M.; Rötter, R.P.; Waha, K.; Wallach, D.; White, J.W.; Wilkens, P.; Williams, J.R.; Wolf, J.; Yin, X.; Yoshida, H.; Zhang, Z.; Zhu, Y. |
| Title |
A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration |
Type |
Journal Article |
| Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
| Volume |
214-215 |
Issue |
|
Pages |
483-493 |
| Keywords |
climate change; crop model; emulator; meta-model; statistical model; yield; climate-change; wheat yields; metaanalysis; uncertainty; simulation; impacts |
| Abstract |
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. |
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English |
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Edition |
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| ISSN |
0168-1923 |
ISBN |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4714 |
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| Author |
Müller, C.; Robertson, R.D. |
| Title |
Projecting future crop productivity for global economic modeling |
Type |
Journal Article |
| Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
| Volume |
45 |
Issue |
1 |
Pages |
37-50 |
| Keywords |
climate change; crop modeling; agricultural productivity; land use; greenhouse-gas emissions; soil organic-carbon; sub-saharan africa; climate-change; elevated co2; land-use; system model; wheat yields; maize yields; agriculture |
| Abstract |
Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10-38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change. |
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English |
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Edition |
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| ISSN |
0169-5150 |
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Article |
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Conference |
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| Notes |
CropM, TradeM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4533 |
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| Author |
Siebert, S.; Ewert, F. |
| Title |
Future crop production threatened by extreme heat |
Type |
Journal Article |
| Year |
2014 |
Publication |
Environmental Research Letters |
Abbreviated Journal |
Environ. Res. Lett. |
| Volume |
9 |
Issue |
4 |
Pages |
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| Keywords |
climate-change; simulation-models; wheat yields; day length; temperature; growth; impact; co2; phenology; patterns |
| Abstract |
Heat is considered to be a major stress limiting crop growth and yields. While important findings on the impact of heat on crop yield have been made based on experiments in controlled environments, little is known about the effects under field conditions at larger scales. The study of Deryng et al (2014 Global crop yield response to extreme heat stress under multiple climate change futures Environ. Res. Lett. 9 034011), analysing the impact of heat stress on maize, spring wheat and soya bean under climate change, represents an important contribution to this emerging research field. Uncertainties in the occurrence of heat stress under field conditions, plant responses to heat and appropriate adaptation measures still need further investigation. |
| Address |
2016-10-31 |
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English |
Summary Language |
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Series Issue |
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Edition |
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| ISSN |
1748-9326 |
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Article |
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Conference |
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| Notes |
CropM, ftnotmacsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4813 |
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| Author |
Dumont, B.; Basso, B.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
| Title |
Climatic risk assessment to improve nitrogen fertilisation recommendations: A strategic crop model-based approach |
Type |
Journal Article |
| Year |
2015 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
| Volume |
65 |
Issue |
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Pages |
10-17 |
| Keywords |
climatic variability; stochastically generated weather; lars-wg; crop model; stics; nitrogen management; yield skewness; wheat yield; generic model; stics; management; variability; simulation; field; balances; impact |
| Abstract |
Within the context of nitrogen (N) management, since 1950, with the rapid intensification of agriculture, farmers have often applied much larger fertiliser quantities than what was required to reach the yield potential. However, to prevent pollution of surface and groundwater induced by nitrates, The European Community launched The European Nitrates Directive 91/6/76/EEC. In 2002, in Wallonia (Belgium), the Nitrates Directive has been transposed under the Sustainable Nitrogen Management in Agriculture Program (PGDA), with the aim of maintaining productivity and revenue for the country’s farmers, while reducing the environmental impact of excessive N application. A feasible approach for addressing climatic uncertainty lies in the use of crop models such as the one commonly known as STICS (simulateur multidisciplinaire pour les cultures standard). These models allow the impact on crops of the interaction between cropping systems and climatic records to be assessed. Comprehensive historical climatic records are rare, however, and therefore the yield distribution values obtained using such an approach can be discontinuous. In order to obtain better and more detailed yield distribution information, the use of a high number of stochastically generated climate time series was proposed, relying on the LARS-Weather Generator. The study focused on the interactions between varying N practices and climatic conditions. Historically and currently, Belgian farmers apply 180 kg N ha(-1), split into three equal fractions applied at the tillering, stem elongation and flag-leaf stages. This study analysed the effectiveness of this treatment in detail, comparing it to similar practices where only the N rates applied at the flag-leaf stage were modified. Three types of farmer decision-making were analysed. The first related to the choice of N strategy for maximising yield, the second to obtaining the highest net revenue, and the third to reduce the environmental impact of potential N leaching, which carries the likelihood of taxation if inappropriate N rates are applied. The results showed reduced discontinuity in the yield distribution values thus obtained. In general, the modulation of N levels to accord with current farmer practices showed considerable asymmetry. In other words, these practices maximised the probability of achieving yields that were at least superior to the mean of the distribution values, thus reducing risk for the farmers. The practice based on applying the highest amounts (60-60-100 kg N ha(-1)) produced the best yield distribution results. When simple economical criteria were computed, the 60-60-80 kg N ha(-1) protocol was found to be optimal for 80-90% of the time. There were no statistical differences, however, between this practice and Belgian farmers’ current practice. When the taxation linked to a high level of potentially leachable N remaining in the soil after harvest was considered, this methodology clearly showed that, in 3 years out of 4,30 kg N ha(-1) could systematically be saved in comparison with the usual practice. |
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English |
Summary Language |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1161-0301 |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4646 |
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