• Energy Research

Water-table drawdown across peatlands increases carbon dioxide (CO2) and reduces methane (CH4) emissions. The net climatic effect remains unclear. Based on global observations from 130 sites, we found a positive (warming) net climate effect of water-table drawdown. Using a machine-learning-based upscaling approach, we predict that peatland water-table drawdown driven by climate drying and human activities will increase CO2 emissions by 1.13 (95% interval: 0.88–1.50) Gt yr−1 and reduce CH4 by 0.26 (0.14–0.52) GtCO2-eq yr−1, resulting in a net increase of greenhouse gas of 0.86 (0.36–1.36) GtCO2-eq yr−1 by the end of the twenty-first century under the RCP8.5 climate scenario. This drops to 0.73 (0.2–1.2) GtCO2-eq yr−1 under RCP2.6. Our results point to an urgent need to preserve pristine and rehabilitate drained peatlands to decelerate the positive feedback among water-table drawdown, increased greenhouse gas emissions and climate warming. The climate impact of water-table drawdown in peatlands is unclear as carbon dioxide emissions increase and methane emissions decrease due to drying. This study shows decreasing water-table depth results in net greenhouse gas emissions from global peatlands, despite reducing methane emissions.

AbstractThe European Green Deal aims to reduce the use of chemical pesticides by half by 2030. Decision support systems are tools to help farmers schedule fungicide spraying based on disease risk and can reduce fungicide application frequency and overall use. However, the potential benefit of decision support systems compared to traditional calendar-based strategies has not yet been rigorously quantified. Here we synthesise 80 experiments and show that globally decision support systems can reduce fungicide treatments by at least 50% without compromising disease control. For a given fixed number of fungicide sprays, decision support systems were as effective as calendar-based programs in reducing disease incidence. When the number of sprays was halved, the increase in disease incidence was lower for decision support system-based strategies than calendar-based strategies. Our findings suggest that decision support systems can reduce fungicide use while limiting the risk to plant health and resistance development.

Future climate change is expected to affect wheat yields. However, it is uncertain if the overall change in climate will result in wheat yield increases or decreases. This is due to the opposing effects of temperature, precipitation, and CO2 concentration on wheat yields. In this study, a meta-analysis of simulated yield change was conducted to identify the levels of temperature, precipitation and CO2 concentration that result in increasing or decreasing wheat yields. With data from 90 studies using computer modeling, we found that more than 50% of the simulated relative yield change resulted in yield losses when mean temperature change is higher than 2.3 degrees C, or mean precipitation change is null or less, or when CO2 concentration is lower than 395 ppm. A statistical model relating relative yield change to the three considered climatic variables was used to explore a large range of climate change scenarios. Results showed that, in average, the effects of high CO2 concentrations (>640 ppm) outweighed the effects of increasing temperature (up to +2 degrees C) and moderate declines in precipitation (up to 20%), leading to increasing yields. However, these results varied greatly from site to site, likely due to differences in topography, soils and farming practices. These results also do not take into account the effects of pests, diseases and weeds or climate variability, which may act to decrease wheat yields. (C) 2013 Elsevier B.V. All rights reserved.

AbstractReliable estimates of the impacts of climate change on crop production are critical for assessing the sustainability of food systems. Global, regional, and site-specific crop simulation studies have been conducted for nearly four decades, representing valuable sources of information for climate change impact assessments. However, the wealth of data produced by these studies has not been made publicly available. Here, we develop a global dataset by consolidating previously published meta-analyses and data collected through a new literature search covering recent crop simulations. The new global dataset builds on 8314 simulations from 203 studies published between 1984 and 2020. It contains projected yields of four major crops (maize, rice, soybean, and wheat) in 91 countries under major emission scenarios for the 21st century, with and without adaptation measures, along with geographical coordinates, current temperatures, local and global warming levels. This dataset provides a basis for a comprehensive understanding of the impacts of climate change on crop production and will facilitate the rapidly developing data-driven machine learning applications.

Abstract Climate change is known to impact crop yields, mainly through increased temperatures, changing rainfall patterns and increasing CO2 concentration in the atmosphere. Although the potential effects of each of these factors have been discussed in a number of separate studies, no recent synthesis has been published to provide quantitative estimates of climate change impacts on crop yields, with or without adaptation strategies. In this paper, we synthetize a broad range of experimental or modeling studies to estimate, at the global scale, crop yield changes resulting from the marginal and combined effects of temperature, CO2 concentration and precipitation, with and without adaptation strategies. Crop yield sensitivities are estimated by distinguishing between C3 and C4 crops. For C3 crops, our results show that the positive effects of adaptation (+7.25 %) and CO2 (+9% for +100 ppm) are high enough to offset the negative effects of temperature increase (-2.4 % for +1 °C), even at +4 °C. On the other hand, for maize (i.e., the only C4 plant species in our database) the somewhat low positive effect from increased CO2 concentration and the absence of a significant effect of adaptation lead to higher yield losses, in the order of -10 % for +4 °C. The minimum level of CO2 concentration increase requested to achieve a yield gain under increased temperature conditions is much higher for maize than for C3 crops, in particular for wheat. The estimated effects of adaptation are uncertain, especially for soybean and rice, but also for maize, where the absence of a significant adaptation effect is probably at least partly due to limited data availability. Our results demonstrate that CO2 effects on crop yields should not be overlooked in foresight studies on the impacts of climate change. Our analysis also highlights the importance of improving our knowledge of how effective adapation strategies are in mitigating the impact of climate change.

Abstract Multi-model forecasting has drawn some attention in crop science for evaluating effect of climate change on crop yields. The principle is to run several individual process-based crop models under several climate scenarios in order to generate ensembles of output values. This paper describes a simple Bayesian method – called Bayes linear method – for updating ensemble of crop model outputs using yield observations. The principle is to summarize the ensemble of crop model outputs by its mean and variance, and then to adjust these two quantities to yield observations in order to reduce uncertainty. The adjusted mean and variance combine two sources of information, i.e., the ensemble of crop model outputs and the observations. Interestingly, with this method, observations collected under a given climate scenario can be used to adjust mean and variance of the model ensemble under a different scenario. Another advantage of the proposed method is that it does not rely on a separate calibration of each individual crop model. The uncertainty reduction resulting from the adjustment of an ensemble of crop models to observations was assessed in a numerical application. The implementation of the Bayes linear method systematically reduced uncertainty, but the results showed the effectiveness of this method varied in function of several factors, especially the accuracy of the yield observation, and the covariance between the crop model output and the observation.

AbstractSimulations of crop yield due to climate change vary widely between models, locations, species, management strategies, and Representative Concentration Pathways (RCPs). To understand how climate and adaptation affects yield change, we developed a meta‐model based on 8703 site‐level process‐model simulations of yield with different future adaptation strategies and climate scenarios for maize, rice, wheat and soybean. We tested 10 statistical models, including some machine learning models, to predict the percentage change in projected future yield relative to the baseline period (2000–2010) as a function of explanatory variables related to adaptation strategy and climate change. We used the best model to produce global maps of yield change for the RCP4.5 scenario and identify the most influential variables affecting yield change using Shapley additive explanations. For most locations, adaptation was the most influential factor determining the projected yield change for maize, rice and wheat. Without adaptation under RCP4.5, all crops are expected to experience average global yield losses of 6%–21%. Adaptation alleviates this average projected loss by 1–13 percentage points. Maize was most responsive to adaptive practices with a projected mean yield loss of −21% [range across locations: −63%, +3.7%] without adaptation and −7.5% [range: −46%, +13%] with adaptation. For maize and rice, irrigation method and cultivar choice were the adaptation types predicted to most prevent large yield losses, respectively. When adaptation practices are applied, some areas are predicted to experience yield gains, especially at northern high latitudes. These results reveal the critical importance of implementing adequate adaptation strategies to mitigate the impact of climate change on crop yields.