Tools for projecting crop productivity under a range of conditions, and assessing adaptation options, are an important part of the endeavour to prioritize investment in adaptation. We present ensemble projections of crop productivity that account for biophysical processes, inherent uncertainty and adaptation, using spring wheat in Northeast China as a case study. A parallel 'vulnerability index' approach uses quantitative socio-economic data to account for autonomous farmer adaptation.The simulations show crop failure rates increasing under climate change, due to increasing extremes of both heat and water stress. Crop failure rates increase with mean temperature, with increases in maximum failure rates being greater than those in median failure rates. The results suggest that significant adaptation is possible through either socio-economic measures such as greater investment, or biophysical measures such as drought or heat tolerance in crops. The results also show that adaptation becomes increasingly necessitated as mean temperature and the associated number of extremes rise. The results, and the limitations of this study, also suggest directions for research for linking climate and crop models, socio-economic analyses and crop variety trial data in order to prioritize options such as capacity building, plant breeding and biotechnology.
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Motivated by persistent predictions of warming and drying in the entire Mediterranean and other regions, we have examined the interactions of intrinsic water-use efficiency (W(i)) with environmental conditions in Pinus halepensis. We used 30-year (1974-2003) tree-ring records of basal area increment (BAI) and cellulose (13)C and (18)O composition, complemented by short-term physiological measurements, from three sites across a precipitation (P) gradient (280-700 mm) in Israel. The results show a clear trend of increasing W(i) in both the earlywood (EW) and latewood (LW) that varied in magnitude depending on site and season, with the increase ranging from ca. 5 to 20% over the study period. These W(i) trends were better correlated with the increase in atmospheric CO(2) concentration, C(a), than with the local increase in temperature (~0.04°C year(-1)), whereas age, height and density variations had minor effects on the long-term isotope record. There were no trends in P over time, but W(i) from EW and BAI were dependent on the interannual variations in P. From reconstructed C(i) values, we demonstrate that contrasting gas-exchange responses at opposing ends of the hydrologic gradient underlie the variation in W(i) sensitivity to C(a) between sites and seasons. Under the mild water limitations typical of the main seasonal growth period, regulation was directed at increasing C(i)/C(a) towards a homeostatic set-point observed at the most mesic site, with a decrease in the W(i) response to C(i) with increasing aridity. With more extreme drought stress, as seen in the late season at the drier sites, the response was W(i) driven, and there was an increase in the W(i) sensitivity to C(a) with aridity and a decreasing sensitivity of C(i) to C(a). The apparent C(a)-driven increases in W(i) can help to identify the adjustments to drying conditions that forest ecosystems can make in the face of predicted atmospheric change.
Estimates of the response of crops to climate change rarely quantify the uncertainty inherent in the simulation of both climate and crops. We present a crop simulation ensemble for a location in India, perturbing the response of both crop and climate under both baseline (12 720 simulations) and doubled-CO 2 (171 720 simulations) climates. Some simulations used parameter values representing genotypic adaptation to mean temperature change. Firstly, observed and simulated yields in the baseline climate were compared. Secondly, the response of yield to changes in mean temperature was examined and compared to that found in the literature. No consistent response to temperature change was found across studies. Thirdly, the relative contribution of uncertainty in crop and climate simulation to the total uncertainty in projected yield changes was examined. In simulations without genotypic adaptation, most of the uncertainty came from the climate model parameters.Comparison with the simulations with genotypic adaptation and with a previous study suggested that the relatively low crop parameter uncertainty derives from the observational constraints on the crop parameters used in this study. Fourthly, the simulations were used, together with an observed dataset and a simple analysis of crop cardinal temperatures and thermal time, to estimate the potential for adaptation using existing cultivars. The results suggest that the germplasm for complete adaptation of groundnut cultivation in western India to a doubled-CO 2 environment may not exist. In conjunction with analyses of germplasm and local management practices, results such as this can identify the genetic resources needed to adapt to climate change. KEY WORDS: Adaptation · Climate change impacts · Crop growth model · General circulation modelResale or republication not permitted without written consent of the publisher
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