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Abstract: Crop models often simulate drought impacts with full and no irrigation scenarios, while planners are more interested in whether the current available irrigation water can cope with the future more serious droughts. This paper addresses a key constraint common to modeling studies: the limited representation of actual irrigation water supply. We present a data-driven approach to identify a benchmark for agronomic drought risk levels as defined by water availability thresholds at the baseline climate (1980–2008) using reported crop yields, climate and irrigation statistics. Then, holding the current irrigation supplies, we adopted Bayesian formula to estimate magnitude of the future water availability and the associated probability of crops yields being decreased to rainfall-deficiency under climate conditions in 2030s (2020–2040) based on the RegCM3 climate model output driven by IPCC SRES A1B scenario. Results reveal that future drought stress would overwhelm the irrigation capacity of current supplies in northern and western China, while drought remains at baseline climate levels in the central, eastern and southern regions. The largest increases in the probability of projected drought risk were in northeast and southwest, ranging from 14% to 28% above baseline climate. Regional drought impacts for grain self sufficiency are discussed. [Copyright &y& Elsevier]

Abstract: For rice (Oryza sativa L.), simulation models like ORYZA2000 and CERES-Rice have been used to explore adaptation options to climate change and weather-related stresses (drought, heat). Output of these models is very sensitive to accurate modelling of crop development, i.e. phenology. What has to date received little attention in phenology calibration is the temperature range within which phenological models are accurate. Particularly the possible correlation between temperature and phenology prediction error has received little attention, although there are indications that such correlation exists, in particular in the study by . The implication of such correlation is that a phenology model that is accurate within the calibration temperature range can be less accurate at higher temperatures where it can systematically overestimate or underestimate the duration of the phase from emergence to flowering. We have developed a new rice phenology calibration program that is consistent with ORYZA2000 concepts and coding. The existing calibration program DRATES of ORYZA2000 requires an assumption of default cardinal temperatures (8, 30 and 42°C) and then calculates cultivar specific temperature sums and development rates. Our new program estimates all phenological parameters simultaneously, including the cardinal temperatures. Applied to nine large datasets from around the world we show that the use of default cardinal temperatures can lead to correlation between temperature and phenology prediction error and temperature and RMSE values in the order of 4–18 days for the period from emergence to flowering. Our new program avoids such correlation and reduces phenology prediction errors to 3–7 days (RMSE). Our results show that the often made assumption of a rapid decrease in development rate above the optimal temperature can lead to poorer predictions and systematic errors. We therefore caution against using default phenological parameters for studies where temperatures may fall outside the range for which the phenological models have been calibrated. In particular, this applies to climate change studies, were this could lead to highly erroneous conclusions. More phenological research with average growing season temperatures above the optimum, in the range of 32–40°C, is needed to establish which phenological model best describes phenology in this temperature range. [Copyright &y& Elsevier]

Abstract: This empirical study (i) assessed rice yield responses to recent climate change at experiment stations, in counties and in provinces of China for the period of 1981–2005 and (ii) identified the climatic drivers determining the trend of yields at each spatial scale. Our empirical results, based on 20 experiment stations during study periods of 14–25 years, indicate that rice yields were positively correlated to solar radiation, which primarily drives yield variation. At most stations, yields were positively correlated to temperature and there was no significant negative correlation between them. Therefore, our empirical results argue against the often-cited hypothesis of lower yields with higher temperature. We explain this by the positive correlation between temperature and radiation at our stations. Empirical analysis to yield at a regional scale (20 counties and 22 provinces) indicates a varying climate to yield relationships. In some places, yields were positively regressed with temperature when they were also positively regressed with radiation, showing the similar pattern at above experiment stations. But, in others, lower yield with higher temperature was accompanied by positive correlation between yield and rainfall, which was not happened at stations. We explain this by irrigation water availability, which played a crucial role in determining climatic effects (radiation or rainfall) on yield variability at a regional scale in China. However, temperature''s negative effect is still weak at any scale. This study showed how rice yields respond to recent climate change from 1981 to 2005 at station and regional scales in China and identifies the major climatic driver for yield variation. The empirical findings presented here provide a foundation for anticipating climate change impacts on rice production in China. [Copyright &y& Elsevier]

Abstract: Current modeling studies on the impacts of climate change on agriculture widely assume that thermal time accumulation of crops during the growing season remains constant under various climate conditions. However, in this study, a 20-year single rice variety, experimental dataset indicates that the thermal time accumulation for the entire growing season is not constant. As a result, a crop model based on constant thermal time accumulation significantly underestimates the observed phenological trend exhibited over the two decades of research—despite comparably accurate simulations of short periods. This deviation can result in misleading yield simulations, whereas the model simulations, using observed phenology data, show a similar yield trend as the observation. This study casts serious doubt on the assumptions of constant thermal time accumulation made in previous modeling studies, and, moreover, it highlights the critical requirements needed to improve phenology simulations on a larger scale so that predictions of the eventual yield trends due to climate change can more accurately reflect the results of yield trends in reality. [Copyright &y& Elsevier]

Abstract: The correlations between ENSO (El Niño/Southern Oscillation) and crops in China have been recognized, but the research focusing on the causes behind the correlations still remains incomplete. In this study, we concentrate on the causal relationship between ENSO and rice yields in North and Northwest China. We found that there was an inconsistency in the observed correlation between rice yields and the occurrence of ENSO events during various periods from 1960 to 2004 in most provinces. Rice was vulnerable to El Niño events before 1980, while it seemed to benefit from the occurrence of such events following 1980. We established the reason for the change through a combination of mechanistic modeling and empirical statistical analysis. We concluded that much of this inconsistency in yield responses to ENSO can be attributed to the development of water supply systems throughout our study region. The meteorological impacts associated with ENSO events varied with water regime. This important finding provided a key explanation into the interaction processes between ENSO climate variability and the water supply in the rice production system, and further confirmed the importance of considering interactive effects between climate variability and human adaptation practices when assessing climate change impacts on agricultural variability. [Copyright &y& Elsevier]

l GPP and land carbon storage in China are simulated using two climate forcings l Land cover change (LCC) is derived from satellite-based land cover l LAI is predicted to increase during 2001-2012, consistent with observations l CO 2 fertilization and LCC make comparable contributions to GPP trends l LCC makes the dominant contributions to the increased carbon storage in China China is experiencing fast afforestation and sensible climate change in recent decades. However, their impacts on regional carbon cycle remain unclear. Here, we explore the effects of climate and land cover change (LCC) on ecosystem productivity and carbon storage in China during 2001-2012 by forcing a dynamic vegetation model with satellite-based land cover and two different climate datasets. The model predicts a significant increase of 0.065 m2 m−2 decade−1 in annual mean leaf area index, higher than the observed trend of 0.038 m2 m−2 decade−1. Meanwhile, national annual total gross primary productivity increases by 0.041 ± 0.007 Pg C yr−2, which is contributed by CO 2 fertilization (0.035 ± 0.001 Pg C yr−2), LCC (0.036 ± 0.001 Pg C yr−2), and climate change (-0.032 ± 0.01 Pg C yr−2). Consequently, the total land carbon storage in China increases by 0.99 Pg C yr−1, to which the LCC alone contributes 0.77 Pg C yr−1. The maximum enhancement of >1 g C m−2 yr−1 in land carbon stock is found over the central west, where a large fraction of shrublands is converted to forests. Our results suggest that afforestation in China provides a strong mitigation to the regional carbon emissions during the 2000s. [ABSTRACT FROM AUTHOR]

Northeast China (NEC) is one of the most important rice production areas in China, accounting for approximately 15% of its total national rice production, and is one of the regions most vulnerable to global climate change. Knowing crops’ potential yields is crucial to understanding comparisons of different cultivars, crops and environments as well as plausible future increases and limits to crop yields. To mitigate the impacts of climate change and enhance food security, investigating yield potential (YP), attainable yield (YP A ), nitrogen-limited potential yield (YP N ), and actual farmers’ yield (Y a ) as well as rice yield gaps and their causes in NEC under the climate change background is necessary. In this study, the ORYZA (v3) crop model was calibrated and validated for rice phenology and yields. The validated model was then used to determine YP, YP A and YP N values as well as yield gaps caused by nitrogen management factors (YG AN ) and other agronomic and socioeconomic factors (YG Na ) for five agroclimatic zones in NEC from 1981 to 2010. The regional area-weighted mean YP levels estimated by the model were 12.9 t ha −1 for YP and 11.6 t ha −1 for YP A with decreasing trends and 8.7 t ha −1 for YP N with an increasing tendency. The Y a was 6.8 t ha −1 , with a significant increase of 1.1 t ha − 1 per decade (p < 0.01), and farmers achieved 58.6% of the YP. The YP A accounted for 80.9–94.8% of the YP in NEC. The regional exploitable yield gap (YG E ) was 4.8 t ha −1 . The regional yield gaps were shrinking at 0.47 and 0.88 t ha −1 per decade for YG AN and YG Na , respectively. In conclusion, because of the persistently large yield gap between farmers and YP N , YP A provides the government and farmers an opportunity to significantly increase rice production by controlling socioeconomic factors and adopting high-yield agronomic management practices based on site-specific conditions, including optimized irrigation and fertilization practices and “super” rice cultivars. [ABSTRACT FROM AUTHOR]