Spring phenology of cotton bollworm affects wheat yield

Climate change has changed numerous species phenologies. Understanding the asynchronous responses between pest insects and host plants to climate change is helpful in improving integrated pest management. It is necessary to use long‐term data to analyze the effects of climate change on cotton bollworm and wheat anthesis. Data for cotton bollworm, wheat yield, and wheat anthesis collected since 1990 were analyzed using linear regression and partial least‐squares regression, as well as the Mann–Kendall test. The results showed that warmer temperatures in the spring advanced the phenologies of cotton bollworm and wheat anthesis, but the phenology changes in overwintering cotton bollworm were faster than those in wheat anthesis, and the eclosion period of overwintering was prolonged, resulting in an increase in overwintering adult abundance. This might lead to more first‐generation larvae and subsequent wheat damage. An early or late first‐appearance date significantly affected the eclosion days. The abrupt changes of phenologies in cotton bollworm, wheat anthesis, and climate were asynchronous, but the abrupt phenology changes occurred after or around the climate abrupt change, especially after or around the abrupt changes of temperature in March and April. The expansion of asynchronous responses in the change rate of wheat anthesis and overwintering cotton bollworm would likely decrease wheat yield due to climate warming in the future. Accumulated temperature was the major affecting factor on the first eclosion date (t₁), adult abundance, and eclosion days. Temperatures in March and April and precipitation in the winter mainly affected the prepeak date (t₂), peak date (t₃), and postpeak date (t₄), respectively, and these factors indirectly affected wheat yield. Thus, the change in the spring phenology of the cotton bollworm and wheat anthesis, and hence wheat yield, was affected by climate warming.     

Impacts of changing climate and agronomic factors on fusarium ear blight of wheat in the UK

Climate change will have direct impacts on fusarium ear blight (FEB) in wheat crops, since weather factors greatly affect epidemics, the relative proportions of species of ear blight pathogens responsible and the production of deoxynivalenol (DON) toxin by two Fusarium species, F. graminearum and F. culmorum. Many established weather-based prediction models do not accurately predict FEB severity in the UK. One weather-based model developed with UK data suggests a slight increase in FEB severity as a direct effect of climate change. However, severity of the disease is likely to increase further due to indirect effects of climate change, such as increased cropping of grain maize, since maize debris is a potent source of inoculum of F. graminearum. To guide strategies for adaptation to climate change, further research on forecasting, management options to reduce mycotoxin production, and breeding for resistant varieties is a high priority for the UK. Adaptation strategies must also consider factors such as tillage regime, wheat cultivar (flowering time and disease resistance) and fungicide use, which also influence the severity of FEB and related toxin production.     

区域气候模式(PRECIS)对黄土高原降水模拟能力的评估

黄土高原水资源短缺,严重制约其社会经济发展;全球变暖背景下,需要对该区水资源状况进行详细的影响评估。区域气候模式可提供气候变化情景下的数据,但模式的模拟精度直接影响评估结果。为此利用ERA40再分析数据作为边界条件驱动PRECIS,从降水频率、降水量和极端事件3个方面,评估了PRECIS对黄土高原1960—2000年降水的模拟能力。结果表明,PRECIS能够模拟出各要素东南-西北方向变化的空间分布特征,还可模拟出整体的时间变化趋势,其中对非汛期的模拟较好,而汛期降水日数和降水量等被严重高估;并且涉及干旱的指标普遍偏低;还发现对于极端降水事件模式对强度指标的模拟能力优于频率指标。因此,还需要进一步探讨订正方法,才能更好的应用于气候变化水文效应评估。     

Impact of derived global weather data on simulated crop yields

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.     

不同气象数据空间内插对区域玉米生长模拟结果的影响

作物模型区域模拟已成为作物模型应用的一个新方向。运用作物模型进行区域研究时,遇到的问题之一就是输入模型的空间数据质量问题,研究不同空间内插法获得的气象数据对作物模型区域模拟结果的影响,可以为区域模拟对输入数据的敏感性研究提供一定的参考。利用区域校准的CERES-Maize模型,将3类内插方法(几何内插、统计内插、动力模型内插)产生的网格化天气数据分别输入到CERES-Maize模型中,模拟了50km×50km网格水平下1961—1990年我国玉米生产状况,并选取1980—1990年模拟的平均产量与同期农调队调查产量进行比较,以了解区域模拟中,不同空间内插方法所得的逐日气象数据对区域模拟结果的影响。结果表明:(1)作物模型区域应用时,所采用的3种内插方法都能满足作物模型区域模拟对网格化天气数据的要求,采用3种天气数据的区域模拟结果都能反映出玉米平均产量的空间变化特征,与网格调查平均产量之间具有极显著的相关关系,但采用不同内插天气数据对模拟结果造成了8%以内的偏差。(2)采用不同内插天气数据,在进行作物区域模拟时,各方法的模拟结果之间呈极显著的相关关系,但这些模拟结果之间,在全国大部分地区是差异显著。     

Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding

Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed. Grain quality is paramount in determining its value and downstream use. While we know that climate change threatens global crop yields, a better understanding of impacts on wheat end-use quality is also critical. Combining quantitative genetics with climate model outputs, we investigated UK-wide trends in genotypic adaptation for wheat quality traits. In our approach, we augmented genomic prediction models with environmental characterisation of field trials to predict trait values and climate effects in historical field trial data between 2001 and 2020. Addition of environmental covariates, such as temperature and rainfall, successfully enabled prediction of genotype by environment interactions (G × E), and increased prediction accuracy of most traits for new genotypes in new year cross validation. We then extended predictions from these models to much larger numbers of simulated environments using climate scenarios projected under Representative Concentration Pathways 8.5 for 2050–2069. We found geographically varying climate change impacts on wheat quality due to contrasting associations between specific weather covariables and quality traits across the UK. Notably, negative impacts on quality traits were predicted in the East of the UK due to increased summer temperatures while the climate in the North and South-west may become more favourable with increased summer temperatures. Furthermore, by projecting 167,040 simulated future genotype–environment combinations, we found only limited potential for breeding to exploit predictable G × E to mitigate year-to-year environmental variability for most traits except Hagberg falling number. This suggests low adaptability of current UK wheat germplasm across future UK climates. More generally, approaches demonstrated here will be critical to enable adaptation of global crops to near-term climate change.     

气候变化对我国干旱/半干旱区小麦生产影响的模拟研究

利用随机天气模型,将气候模式对大气中CO₂倍增时预测的气候情景与CERES-小麦模式相连接,研究了气候变化对我国冬小麦和春小麦生产的可能影响。并对水分、温度、CO₂综合对小麦的作用进行初步模拟分析。所得结论为:①气候变化后小麦发育将加快,生育期缩短,春小麦生育期缩短的绝对数和相对数均小于冬小麦。②北方十个站点小麦生产的最适水分条件在不同站点、不同气候情景下都有所不同。最适水分条件变幅在40%～80%。③在不考虑CO₂对小麦影响的情况下,由于热量充足,只要水分条件适宜,未来我国北方干旱、半干旱地区小麦产量整体都有增产趋势。如果考虑CO₂,增产效果更加明显。     

2011-2050年黄淮海冬小麦、夏玉米气候生产潜力评价

基于区域气候模式PRECIS输出的未来B2气候情景(2011-2050年)逐日资料以及基准气候时段(1961-1990年)的逐日资料,应用农业生态区域(AEZ)模型,对2011-2050年我国黄淮海地区冬小麦、夏玉米气候生产潜力时空变化特征进行预测.结果表明:基准气候时段下,我国黄淮海地区冬小麦、夏玉米气候生产潜力的空间分布呈现一定的区域分异规律,总体均呈东南高、西北低的趋势,且同纬度地区的沿海高于内陆.1961-1990年,冬小麦、夏玉米气候生产潜力的变化幅度分别在3893 ～11000和5908～12000kg·hm-2.未来B2气候情景下,冬小麦、夏玉米气候生产潜力的年际变化很大,这与该时期作物生长发育光、温、水的匹配程度有关.冬小麦、夏玉米分别在2011-2030年和2021-2040年间气候生产潜力的增加趋势非常明显,开发潜力很大.在保持现有生产状况下,未来B2气候情景下,2011-2050年冬小麦气候生产潜力在空间上总体呈现明显的区域分异,表现为东南地区与西北地区的反向变化、沿海地区与内陆地区之间的同向变化;而夏玉米气候生产潜力的区域分异规律不明显.     

Climate change impacts on crop yield and quality with CO2 fertilization in China

A regional climate change model (PRECIS) for China, developed by the UK's Hadley Centre, was used to simulate China's climate and to develop climate change scenarios for the country. Results from this project suggest that, depending on the level of future emissions, the average annual temperature increase in China by the end of the twenty-first century may be between 3 and 4 degrees C. Regional crop models were driven by PRECIS output to predict changes in yields of key Chinese food crops: rice, maize and wheat. Modelling suggests that climate change without carbon dioxide (CO2) fertilization could reduce the rice, maize and wheat yields by up to 37% in the next 20-80 years. Interactions of CO2 with limiting factors, especially water and nitrogen, are increasingly well understood and capable of strongly modulating observed growth responses in crops. More complete reporting of free-air carbon enrichment experiments than was possible in the Intergovernmental Panel on Climate Change's Third Assessment Report confirms that CO2 enrichment under field conditions consistently increases biomass and yields in the range of 5-15%, with CO2 concentration elevated to 550 ppm Levels of CO2 that are elevated to more than 450 ppm will probably cause some deleterious effects in grain quality. It seems likely that the extent of the CO2 fertilization effect will depend upon other factors such as optimum breeding, irrigation and nutrient applications.     

Potential impact of climate change on selected agricultural crops in north-eastern Austria

The vulnerability and adaptation of major agricultural crops to various soils in north‐eastern Austria under a changing climate were investigated. The CERES crop model for winter wheat and the CROPGRO model for soybean were validated for the agrometeorological conditions in the selected region. The simulated winter wheat and soybean yields in most cases agreed with the measured data. Several incremental and transient global circulation model (GCM) climate change scenarios were created and used in the study. In these scenarios, annual temperatures in the selected region are expected to rise between 0.9 and 4.8 °C from the 2020s to the 2080s. The results show that warming will decrease the crop‐growing duration of the selected crops. For winter wheat, a gradual increase in air temperature resulted in a yield decrease. Incremental warming, especially in combination with an increase in precipitation, leads to higher soybean yield. A drier climate will reduce soybean yield, especially on soils with low water storage capacity. All transient GCM climate change scenarios for the 21st century, including the adjustment for only air temperature, precipitation and solar radiation, projected reductions of winter wheat yield. However, when the direct effect of increased levels of CO₂ concentration was assumed, all GCM climate change scenarios projected an increase in winter wheat yield in the region. The increase in simulated soybean yield for the 21st century was primarily because of the positive impact of warming and especially of the beneficial influence of the direct CO₂ effect. Changes in climate variability were found to affect winter wheat and soybean yield in various ways. Results from the adaptation assessments suggest that changes in sowing date, winter wheat and soybean cultivar selection could significantly affect crop production in the 21st century.     

2011—2050年黄淮海冬小麦、夏玉米气候生产潜力评价

基于区域气候模式PRECIS输出的未来B2气候情景（2011—2050年）逐日资料以及基准气候时段（1961—1990年）的逐日资料,应用农业生态区域(AEZ)模型,对2011—2050年我国黄淮海地区冬小麦、夏玉米气候生产潜力时空变化特征进行预测.结果表明： 基准气候时段下,我国黄淮海地区冬小麦、夏玉米气候生产潜力的空间分布呈现一定的区域分异规律,总体均呈东南高、西北低的趋势,且同纬度地区的沿海高于内陆.1961—1990年,冬小麦、夏玉米气候生产潜力的变化幅度分别在3893～11000和5908～12000 kg·hm^-2.未来B2气候情景下,冬小麦、夏玉米气候生产潜力的年际变化很大,这与该时期作物生长发育光、温、水的匹配程度有关.冬小麦、夏玉米分别在2011—2030年和2021—2040年间气候生产潜力的增加趋势非常明显,开发潜力很大.在保持现有生产状况下,未来B2气候情景下,2011—2050年冬小麦气候生产潜力在空间上总体呈现明显的区域分异,表现为东南地区与西北地区的反向变化、沿海地区与内陆地区之间的同向变化;而夏玉米气候生产潜力的区域分异规律不明显.     

Observed changes in winter wheat phenology in the North China Plain for 1981–2009

Climate change in the last three decades could have major impacts on crop phenological development and subsequently on crop productivity. In this study, trends in winter wheat phenology are investigated in 36 agro-meteorological stations in the North China Plain (NCP) for the period 1981–2009. The study shows that the dates of sowing (BBCH 00), emergence (BBCH 10) and dormancy (start of dormancy) are delayed on the average by 1.5, 1.7 and 1.5 days/decade, respectively. On the contrary, the dates of greenup (end of dormancy), anthesis (BBCH 61) and maturity (BBCH 89) occur early on the average by 1.1, 2.7 and 1.4 days/decade, respectively. In most of the investigated stations, GP2 (dormancy to greenup), GP3 (greenup to anthesis) and GP0 (entire period from emergence to maturity) of winter wheat shortened during the period 1981–2009. Due, however, to early anthesis, grain-filling stage occurs at lower temperatures than before. This, along with shifts in cultivars, slightly prolongs GP4 (anthesis to maturity). Comparison of field-observed CERES (Crop Environment Resource Synthesis)-wheat model-simulated dates of anthesis and maturity suggests that climate warming is the main driver of the changes in winter wheat phenology in the NCP. The findings of this study further suggest that climate change impact studies should be strengthened to adequately account for the complex responses and adaptations of field crops to this global phenomenon.     

Simulating effects of climate change on boreal ecosystem carbon pools in central Canada

Aim Possible effects of current and future climates on boreal vegetation dynamics and carbon (C) cycling were investigated using the CENTURY 4.0 soil process model and a modified version of the FORSKA2 forest patch model. Location Eleven climate station locations distributed along a transect across the boreal zone of central Canada. Methods Both models were driven by detrended long-term monthly climate data. Using a climate change signal derived from the GISS general circulation model (GCM) 2×CO₂ equilibrium climate scenario, the output from the two models was then used to compare simulated current and possible future total ecosystem C storage at the climate station locations. Results After allowing for their different underlying structures, comparison of output from both models showed good agreement with local field data under current climate conditions. CENTURY 4.0 was able to reproduce spatial variation in soil and litter C densities satisfactorily but tended to overestimate biomass productivity. FORSKA2 reproduced aboveground biomass productivity and spatially averaged biomass densities relatively well. Under the GISS 2×CO₂ scenario, both models generally predicted small increases in aboveground biomass C density for forest and tundra locations, but CENTURY 4.0 predicted greater decreases in soil and litter pools, for overall decreases in ecosystem C storage in the range 16–19%. Main conclusions With some caveats, results imply that effects of increased precipitation (as simulated by the GISS GCM) would more than compensate for any negative effects of increased temperature on forest growth. Increased temperature would also increase decomposition rates of soil and litter organic matter, however, for a net overall decrease in total ecosystem C storage.     

气候变化背景下我国农业热量资源的变化趋势及适应对策

根据区域气候模式PRECIS输出的未来A2气候情景（2011-2050年）以及基准气候时段（1961-1990年）的逐日资料,对2011-2050年我国农业热量资源的变化趋势进行了预测.结果表明: 与1961-1990年相比,未来A2气候情景下,2011-2050年我国大部分地区的平均无霜期日数延长趋势明显,主要表现为终霜冻日的提前和初霜冻日的推迟;各地日均气温稳定通过0 ℃的持续日数也明显延长,大部分地区延长了1～14 d,其中2041-2050年,青藏地区大部、长江中下游地区大部、甘新地区西部和西南地区北部均可延长49 d;我国大部分地区≥0 ℃积温均呈增加趋势.为适应未来农业热量资源的变化,应进一步调整农业种植制度、优化农业生产布局和发展生物技术等,以实现我国农业的可持续发展.     

A process‐based approach to modelling impacts of climate change on the damage niche of an agricultural weed

Predicting the impact of climate change on the damage niche of an agricultural weed at a local scale requires a process‐based modelling approach that integrates local environmental conditions and the differential responses of the crop and weed to change. A simulation model of the growth and population dynamics of winter wheat and a competing weed, Sirius 2010, was calibrated and validated for the most economically damaging weed in UK cereals, Alopecurus myosuroides. The model was run using local‐scale climatic scenarios generated by the LARS‐WG weather generator and based on the HadCM3 projections for the periods 2046–2065 and 2080–2099 to predict the impact of climate change on the population dynamics of the weed and its effect on wheat yields. Owing to rising CO₂ concentration and its effect on radiation use efficiency of wheat, weed‐free wheat yields were predicted to increase. The distribution of the weed was predicted to remain broadly similar with a possible northward shift in range. Local‐scale variation in the impact of climate change was apparent owing to variation in soil type and water holding capacity. The competitive balance was shifted in favour of the deeper rooted crop under climate change, particularly on sites with lighter soils, owing to more frequent and severe drought stress events. Although the damage niche of A. myosuroides was predicted to reduce under climate change, it is likely that weeds with contrasting physiology, such as C4 species, will be better adapted to future conditions and pose a more serious threat.     

How Early Can the Seeding Dates of Spring Wheat Be under Current and Future Climate in Saskatchewan,Canada?

Background

Shorter growing season and water stress near wheat maturity are the main factors that presumably limit the yield potential of spring wheat due to late seeding in Saskatchewan, Canada. Advancing seeding dates can be a strategy to help producers mitigate the impact of climate change on spring wheat. It is unknown, however, how early farmers can seed while minimizing the risk of spring frost damage and the soil and machinery constraints.

Methodology/principal findings

This paper explores early seeding dates of spring wheat on the Canadian Prairies under current and projected future climate. To achieve this, (i) weather records from 1961 to 1990 were gathered at three sites with different soil and climate conditions in Saskatchewan, Canada; (ii) four climate databases that included a baseline (treated as historic weather climate during the period of 1961–1990) and three climate change scenarios (2040–2069) developed by the Canadian global climate model (GCM) with the forcing of three greenhouse gas (GHG) emission scenarios (A2, A1B and B1); (iii) seeding dates of spring wheat (Triticum aestivum L.) under baseline and projected future climate were predicted. Compared with the historical record of seeding dates, the predicted seeding dates were advanced under baseline climate for all sites using our seeding date model. Driven by the predicted temperature increase of the scenarios compared with baseline climate, all climate change scenarios projected significantly earlier seeding dates than those currently used. Compared to the baseline conditions, there is no reduction in grain yield because precipitation increases during sensitive growth stages of wheat, suggesting that there is potential to shift seeding to an earlier date. The average advancement of seeding dates varied among sites and chosen scenarios. The Swift Current (south-west) site has the highest potential for earlier seeding (7 to 11 days) whereas such advancement was small in the Melfort (north-east, 2 to 4 days) region.

Conclusions/significance

The extent of projected climate change in Saskatchewan indicates that growers in this region have the potential of earlier seeding. The results obtained in this study may be used for adaptation assessments of seeding dates under possible climate change to mitigate the impact of potential warming.     

气候变化对我国华北地区冬小麦发育和产量的影响

验证作物模型在我国华北冬小麦主产区是否适应的基础上,采用作物模型与气候模式相结合的研究方法,定量化地模拟预测了未来100年气候变化对华北冬小麦生产的影响.结果表明,从2000～2004年,华北地区冬小麦产量的模拟值与实测值的变化趋势基本一致,且生育期和产量变化不大.未来100年内华北地区冬小麦的生长期可能会有所缩短,平均缩短8.4 d;产量也会有不同程度的下降,平均减产10.1％.适当采取应对措施可以有效降低冬小麦的减产趋势.     

An improved model for determining degree-day values from daily temperature data

Although using hourly weather data offers the greatest accuracy for estimating growing degree-day values, daily maximum and minimum temperature data are often used to estimate these values by approximating the diurnal temperature trends. This paper presents a new empirical model for estimating the hourly mean temperature. The model describes the diurnal variation using a sine function from the minimum temperature at sunrise until the maximum temperature is reached, another sine function from the maximum temperature until sunset, and a square-root function from then until sunrise the next morning. The model was developed and calibrated using several years of hourly data obtained from five automated weather stations located in California and representing a wide range of climate conditions. The model was tested against an additional data-set at each location. The temperature model gave good results, the root-mean-square error being less than 2.0 °C for most years and locations. The comparison with published models from the literature showed that the model was superior to the other methods. Hourly temperatures from the model were used to calculate degree-day values. A comparison between degree-day estimates determined from the model and those obtained other selected methods is presented. The results showed that the model had the best accuracy in general regardless of the season. Received: 25 October 2000 / Revised: 2 July 2001 / Accepted: 2 July 2001     

Contributions of climatic and crop varietal changes to crop production in the North China Plain,since 1980s

The North China Plain (NCP) is the most important agricultural production area in China. Crop production in the NCP is sensitive to changes in both climate and management practices. While previous studies showed a negative impact of climatic change on crop yield since 1980s, the confounding effects of climatic and agronomic factors have not been separately investigated. This paper used 25 years of crop data from three locations (Nanyang, Zhengzhou and Luancheng) across the NCP, together with daily weather data and crop modeling, to analyse the contribution of changes in climatic and agronomic factors to changes in grain yields of wheat and maize. The results showed that the changes in climate were not uniform across the NCP and during different crop growth stages. Warming mainly occurred during the vegetative (preflowering) growth stage of wheat and maize, while there was a cooling trend or no significant change in temperatures during the postflowering stage of wheat (spring) or maize (autumn). If varietal effects were excluded, warming during vegetative stages would lead to a reduction in the length of the growing period for both crops, generally leading to a negative impact on crop production. However, autonomous adoption of new crop varieties in the NCP was able to compensate the negative impact of climatic change. For both wheat and maize, the varietal changes helped stabilize the length of preflowering period against the shortening effect of warming and, together with the slightly reduced temperature in the postflowering period, extend the length of the grain‐filling period. The combined effect led to increased wheat yield at Zhengzhou and Luancheng; increased maize yield at Nanyang and Luancheng; stabilized wheat yield at Nanyang, and a slight reduction in maize yield at Zhengzhou, compared with the yield change caused entirely by climatic change.     

Global evaluation of a semiempirical model for yield anomalies and application to within‐season yield forecasting

Quantifying the influence of weather on yield variability is decisive for agricultural management under current and future climate anomalies. We extended an existing semiempirical modeling scheme that allows for such quantification. Yield anomalies, measured as interannual differences, were modeled for maize, soybeans, and wheat in the United States and 32 other main producer countries. We used two yield data sets, one derived from reported yields and the other from a global yield data set deduced from remote sensing. We assessed the capacity of the model to forecast yields within the growing season. In the United States, our model can explain at least two‐thirds (63%–81%) of observed yield anomalies. Its out‐of‐sample performance (34%–55%) suggests a robust yield projection capacity when applied to unknown weather. Out‐of‐sample performance is lower when using remote sensing‐derived yield data. The share of weather‐driven yield fluctuation varies spatially, and estimated coefficients agree with expectations. Globally, the explained variance in yield anomalies based on the remote sensing data set is similar to the United States (71%–84%). But the out‐of‐sample performance is lower (15%–42%). The performance discrepancy is likely due to shortcomings of the remote sensing yield data as it diminishes when using reported yield anomalies instead. Our model allows for robust forecasting of yields up to 2 months before harvest for several main producer countries. An additional experiment suggests moderate yield losses under mean warming, assuming no major changes in temperature extremes. We conclude that our model can detect weather influences on yield anomalies and project yields with unknown weather. It requires only monthly input data and has a low computational demand. Its within‐season yield forecasting capacity provides a basis for practical applications like local adaptation planning. Our study underlines high‐quality yield monitoring and statistics as critical prerequisites to guide adaptation under climate change.