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1.
In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate sources of uncertainty. This paper considers the specific aspect of input weather data quality from a Regional Climate Model (RCM) leading to differences in estimates made by three crop models. The availability of hindcast RCM estimates enables comparison of crop model outputs derived from observed and modelled weather data. Errors in estimating the past climate implies biases in future projections, and thus affect modelled crop responses. We investigate the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed (12 UK sites), original (50 × 50 km) and bias corrected downscaled (site-specific) hindcast (1960–1990) weather data from the HadRM3 RCM. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop model estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite evaluated (observed-hindcast) differences. This is likely due to compensating errors in the input weather data and non-linearity in the crop models processes, making interpretation of results problematic. Understanding how biases in climate data manifest themselves in individual crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections and crop model ensembles. The results have implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles.  相似文献   

2.
Crop growth simulation models are increasingly used for regionally assessing the effects of climate change and variability on crop yields. These models require spatially and temporally detailed, location-specific, environmental (weather and soil) and management data as inputs, which are often difficult to obtain consistently for larger regions. Aggregating the resolution of input data for crop model applications may increase the uncertainty of simulations to an extent that is not well understood. The present study aims to systematically analyse the effect of changes in the spatial resolution of weather input data on yields simulated by four crop models (LINTUL-SLIM, DSSAT-CSM, EPIC and WOFOST) which were utilized to test possible interactions between weather input data resolution and specific modelling approaches representing different degrees of complexity. The models were applied to simulate grain yield of spring barley in Finland for 12 years between 1994 and 2005 considering five spatial resolutions of daily weather data: weather station (point) and grid-based interpolated data at resolutions of 10 km × 10 km; 20 km × 20 km; 50 km × 50 km and 100 km × 100 km. Our results show that the differences between models were larger than the effect of the chosen spatial resolution of weather data for the considered years and region. When displaying model results graphically, each model exhibits a characteristic ‘fingerprint’ of simulated yield frequency distributions. These characteristic distributions in response to the inter-annual weather variability were independent of the spatial resolution of weather input data. Using one model (LINTUL-SLIM), we analysed how the aggregation strategy, i.e. aggregating model input versus model output data, influences the simulated yield frequency distribution. Results show that aggregating weather data has a smaller effect on the yield distribution than aggregating simulated yields which causes a deformation of the model fingerprint. We conclude that changes in the spatial resolution of weather input data introduce less uncertainty to the simulations than the use of different crop models but that more evaluation is required for other regions with a higher spatial heterogeneity in weather conditions, and for other input data related to soil and crop management to substantiate our findings. Our results provide further evidence to support other studies stressing the importance of using not just one, but different crop models in climate assessment studies.  相似文献   

3.
Drought stresses arise when the combination of rainfall and soil water supply are insufficient to meet the transpiration needs of the crop. In the Cerrado region of Goiás state, Brazil, summer rainfall is typically greater than 1000 mm. However, drought stress can occur during rain-free periods of only 1–3 weeks, since roots are frequently restricted to shallow depths due to Al-induced acidity in deeper soil layers. If these droughts are frequent, then plant breeding programs need to consider how to develop suitable germplasm for the target population of environments (TPE). A crop simulation model was used to determine patterns of drought stress for 12 locations and >30 environments (6 years × 5–6 planting dates) for short and medium duration rice crops (planted in early summer), and for maize grown either as a 1st or 2nd crop in the summer cycle. Regression analysis of the simulations confirmed the greater yield impact in both crops of drought stress (quantified as the ratio of water-limited to potential transpiration) when it occurred around the time of flowering and early grain-filling. For rice, mild mid-season droughts occurred 40–60% of the time in virgin (0.4 m deep for rice or 0.5 m for maize) soils and improved (0.8 m for rice or 1.0 m for maize) soils, with a yield reduction of <30%. More severe reproductive and grain-filling stress (yield reductions of 50% for rice to 90% for maize) occurred less frequently in rice (<30% of time) and 1st maize crop (< 10% of time). The 2nd maize crop experienced the greatest proportion (75–90%) of drought stresses that reduced yield to <50% of potential, with most of these occasions associated with later planting. The rice breeding station (CNPAF) experiences the same pattern of different drought types as for the TPE, and is largely suitable for early-stage selection of adapted germplasm based on yield potential. However, selection for virgin soil types could be augmented by evaluation on some less-improved soils in the slightly drier parts of the TPE region. Similarly, the drought patterns at the maize research station (CNPMS) and the other maize screening locations are better suited to selection of lines for the improved soil types. Development of lines for the 2nd crop and on more virgin (acidic) soils would require more targeted selection at late planting dates in drier sites.  相似文献   

4.
Uncertainty of crop yield simulation would be affected by weather input data prepared from different sources of climate datasets. Although regional climate data at a high spatial resolution would be useful for the impact assessment of climate change on crop production, little effort has been made to characterize the uncertainty associated with such climate data in terms of crop yield simulations. The objectives of this study were to compare climate scenario data products obtained from a series of downscaling processes and to identify an overall pattern of uncertainty in these climate data in terms of crop yield simulation. Regional climate scenario data from 2011 to 2014 had a spatiotemporal pattern of uncertainty, which differed by meteorological variables and spatial resolution. Overall, the uncertainty of daily minimum temperature was greater than that of maximum temperature. Daily minimum temperature also had relatively greater uncertainty in an early season of crop production, which could result in the cumulative impact on the uncertainty of crop yield simulations. For the uncertainty of climate data at different spatial resolution, climate data at higher spatial resolution, e.g. 1 km, tended to have lower uncertainty than data at resolution of 12.5 km did. Still, the uncertainty of regional climate data was relatively similar between data at resolution of 12.5 km and 1 km in major rice production areas in Korea except in areas near Seosan. This merits further studies to examine actual differences in projected crop yields using regional climate scenario data in the future and to assess the impact of uncertainty associated with regional climate data on crop yield simulation.  相似文献   

5.
It would be preferable to use a reliable crop growth model for studies on climate change impact assessment. The objectives of this study was to evaluate simulation performance for two maize models, including CERES-Maize and IXIM models, included in the DSSAT model (version 4.6) in terms of phenology and yield. Two early maturing cultivars, Chalok#1 and Junda# 6, were grown under controlled environment in plastic houses at Suwon, Korea. Each cultivar, which was sown at four different date in 2013 and 2014, was subjected to four sets of temperature conditions including ambient (AT), AT+1.5°C, AT+3°C, and AT+5°C. In simulations of phenology under given conditions, the anthesis date and grain filling ratio were underestimated, especially when temperature was unusually high, e.g., in 2013. The maize models also had poor accuracy in grain yield, which resulted from the fact that these models had relatively large errors in simulation of kernel number and kernel weight under elevated temperature conditions. In addition, both models were not able to simulate the drastic decrease of kernel number due to heat stress around flowering periods. These results indicated that two maize models would need improvements in simulation of crop response to supra-optimal temperature before they would be used to assess the impact of the climate change on maize yield. This studies merits further study to improve algorithms in phenology simulation at supraoptimal temperature.  相似文献   

6.
Crop models have been widely used in simulating and predicting changes in rice phenology in the major rice production regions of China, however the uncertainties in simulating crop phenology at a large scale and from different models were rarely investigated. In the present study, five rice phenological models/modules (i.e., CERES-Rice, ORYZA2000, RCM, Beta Model, SIMRIW) were firstly calibrated and validated based on a large number of rice phenological observations across China during 1981–2009. The inner workings of the models, as well as the simulated phenological response to climate change/variability, were compared to determine if the models adequately handled climatic changes and climatic variability. Results showed these models simulated rice phenological development over a large area fairly well after calibration, although the relative performance of the models varied in different regions. The simulated changes in rice phenology were generally consistent when temperatures were below the optimum; however varied largely when temperatures were above the optimum. The simulated rice growing season under future climate scenarios was shortened by about 0.45–5.78 days; but in northeastern China, increased temperature variability may prolong the growing season of rice. We concluded more modeling and experimental studies should be conducted to accelerate understanding of rice phenology development under extreme temperatures.  相似文献   

7.
Water deficit is a major factor responsible for soybean yield gap in Southern Brazil and tends to increase under climate change. An alternative to reduce such gap is to identify soybean cultivars with traits associated to drought tolerance. Thus, the aim of this study was to assess soybean adaptive traits to water deficit that can improve yield under current and future climates, providing guidelines for soybean cultivar breeding in Southern Brazil. The following soybean traits were manipulated in the CSM-CROPGRO-Soybean crop model: deeper root depth in the soil profile; maximum fraction of shoot dry matter diverted to root growth under water stress; early reduction of transpiration under mild stress; transpiration limited as a function of vapor pressure deficit; N2 fixation drought tolerance; and sensitivity of grain filling period to water deficit. The yields were predicted for standard and altered traits using climate data for the current (1961–2014) and future (middle-century) scenarios. The traits with greater improvement in soybean yield were deeper rooting profile, with yield gains of ≈300 kg ha−1, followed by transpiration limited as a function of vapor pressure deficit and less drought-induced shortening of the grain filling period. The maximum fraction of shoot dry matter diverted to root and N2 fixation drought tolerance increased yield by less than 75 kg ha−1, while early reduction of transpiration resulted in a small area of country showing gains. When these traits were combined, the simulations resulted in higher yield gains than using any single trait. These results show that traits associated with deeper and greater root profile in the soil, reducing transpiration under water deficit more than photosynthesis, creating tolerance of nitrogen fixation to drought, and reducing sensitivity of grain filling period to water deficit should be included in new soybean cultivars to improve soybean drought tolerance in Southern Brazil.  相似文献   

8.
Effects of climate variability and change on yields of pearl millet have frequently been evaluated but yield responses to combined changes in crop management and climate are not well understood. The objectives of this study were to determine the combined effects of nutrient fertilization management and climatic variability on yield of pearl millet in the Republic of Niger. Considered fertilization treatments refer to (i) no fertilization and the use of (ii) crop residues, (iii) mineral fertilizer and (iv) a combination of both. A crop simulation model (DSSAT 4.5) was evaluated by using data from field experiments reported in the literature and applied to estimate pearl millet yields for two historical periods and under projected climate change. Combination of crop residues and mineral fertilizer resulted in higher pearl millet yields compared to sole application of crop residues or fertilizer. Pearl millet yields showed a strong response to mean temperature under all fertilization practices except the combined treatment in which yields showed higher correlation to precipitation. The crop model reproduced reported yields well including the detected sensitivity of crop yields to mean temperature, but underestimated the response of yields to precipitation for the treatments in which crop residues were applied. The crop model simulated yield declines due to projected climate change by −11 to −62% depending on the scenario and time period. Future crop yields in the combined crop residues + fertilizer treatment were still larger than crop yields in the control treatment with baseline climate, underlining the importance of crop management for climate change adaptation. We conclude that nutrient fertilization and other crop yield limiting factors need to be considered when analyzing and assessing the impact of climate variability and change on crop yields.  相似文献   

9.
The impact of extreme events (such as prolonged droughts, heat waves, cold shocks and frost) is poorly represented by most of the existing yield forecasting systems. Two new model-based approaches that account for the impact of extreme weather events on crop production are presented as a way to improve yield forecasts, both based on the Crop Growth Monitoring System (CGMS) of the European Commission. A first approach includes simple relations – consistent with the degree of complexity of the most generic crop simulators – to explicitly model the impact of these events on leaf development and yield formation. A second approach is a hybrid system which adds selected agro-climatic indicators (accounting for drought and cold/heat stress) to the previous one. The new proposed methods, together with the CGMS-standard approach and a system exclusively based on selected agro-climatic indicators, were evaluated in a comparative fashion for their forecasting reliability. The four systems were assessed for the main micro- and macro-thermal cereal crops grown in highly productive European countries. The workflow included the statistical post-processing of model outputs aggregated at national level with historical series (1995–2013) of official yields, followed by a cross-validation for forecasting events triggered at flowering, maturity and at an intermediate stage. With the system based on agro-climatic indicators, satisfactory performances were limited to microthermal crops grown in Mediterranean environments (i.e. crop production systems mainly driven by rainfall distribution). Compared to CGMS-standard system, the newly proposed approaches increased the forecasting reliability in 94% of the combinations crop × country × forecasting moment. In particular, the explicit simulation of the impact of extreme events explained a large part of the inter-annual variability (up to +44% for spring barley in Poland), while the addition of agro-climatic indicators to the workflow mostly added accuracy to an already satisfactory forecasting system.  相似文献   

10.
Historically, wheat yields in drought‐prone Australian environments have been consistently increasing for over a century. There is currently an agreement that approximately half of that increase is attributable to breeding programmes, but their physiological basis remains poorly documented. In this investigation, we hypothesized that limited whole‐plant transpiration rate (TR) under high atmospheric vapour pressure deficit (VPD) could result in advantageous water conservation and crop yield increase under south Australian conditions. Therefore, TR response to VPD was measured in the 0.9–3.2 kPa range for a group of 23 wheat cultivars that were released from 1890 to 2008. Consistent with a water‐conservation hypothesis, all genotypes displayed a VPD break point (BP) in TR with increasing VPD such that TR was limited at VPD above a BP of about 2 kPa. The BP and slope of TR with increasing VPD above the break point were correlated with the year of release, although the changes were in different directions. Such changes in these transpiration parameters were independent of plant leaf area and only marginally correlated with Zadok's stages. These results indicated that selection over 120 years by breeders for yield increase unconsciously resulted in genotype selection for the expression of the limited‐TR trait.  相似文献   

11.
The possible impact of climate change on frequency and severity of weather extremes is hotly debated among climate scientists. Weather extremes can have a significant impact on agricultural production, but their effect is often unclear; this due to interaction with other factors that affect yield and due to lack of precise definitions of relevant weather extremes. We show that an empirical analysis of historical yields can help to identifying such rare, high impact climate events. A reconstructed time series of ware potato production in Flevoland (The Netherlands) over the last 60 years (1951-2010) enabled us to identify the two main yield affecting weather extremes. In around 10% of the years yield anomalies were larger than −20%. We found that these anomalies could be explained from two weather extremes (and no other), namely a wet start of the growing season and wet end of the growing season. We derived quantitative, meteorological definitions of these extremes. Climate change scenarios for 2050 show either no change or increased frequency of the two extremes. We demonstrate there is large uncertainty about past and future frequencies of the extremes, caused by a lack of sufficiently long historical weather records and uncertainties in climate change projections on precipitation. The approach to identify weather extremes presented here is generally applicable and shows the importance of long term crop and weather observations for investigating key climatic risks to production.  相似文献   

12.
Spatial evaluation of the uncertainty associated with climate data would allow reliable interpretation of simulation results for regional crop yield using gridded climate data as input to a crop growth model. The objective of this study was to examine the spatial uncertainty of regional climate model data through determining optimal seeding date with the ORYZA2000 model for assessment of climate change impact on rice productivity in Korea. The optimal seeding date was determined at each grid point using regional climate model outputs under the RCP 8.5 scenarios. In major rice production areas such as inland plain regions, where temperatures of regional climate data were relatively accurate, the optimal seeding date determined using those gridded data were reasonable. However, areas with complex terrains including areas near bodies of water, e.g. coastal areas, riverbasins, lakes, and mountainous areas, had a relatively large uncertainty of the optimal seeding date determined using the regional climate data. These results indicated that the uncertainty of regional climate data at a high spatial resolution of 12.5 km should be taken into account in the regional impact assessment based on crop growth simulations in Korea. In addition, further studies would be merited to assess the impact of climate change on rice yield at an ultra-high spatial resolution of 1 km in Korea. Crop yields were projected to decrease after the 2020s when crop yield simulations from inland plain areas were considered, which suggested that adaptation strategies should be established and implemented in the near future.  相似文献   

13.
We studied the interaction between Eucalyptus saligna woodlots and maize crop in southern Rwanda. Three sites were selected and in each, a eucalypt woodlot with mature trees and a suitable adjoining crop field of 12.75 m × 30 m was selected. This was split into two plots of 6 m × 12 m and further subdivided into nine sub-plots running parallel to the tree-crop interface. Maize was grown in both 6 m × 12 m plots and one of these received fertiliser. Soil moisture, nutrients and solar radiation were significantly reduced near the woodlots, diminishing grain yield by 80% in the 10.5 m crop-field strip next to the woodlot. This reduction however affects only 10.5% of the maize crop field, leaving 89.5% unaffected. Spreading the loss to a hectare crop field, leads to an actual yield loss of 0.21 t ha−1, equivalent to 8.4%. Expressing yield loss in tree-crop systems usually presented as a percentage of yield recorded near the trees to that obtained in open areas may be misleading. Actual yields should be reported with corresponding crop field areas affected. Variation in grain yield coincided with those for soil moisture, soil N and K; all increasing from the woodlot-maize interface up to 10.5 m and remaining similar to the values in open areas thereafter. Solar radiation continued to increase with distance up to 18 m from the woodlot-maize interface. Harvest index in unfertilised maize exceeded that in the fertilised treatment reflecting the crop’s strategy to allocate resources to grain production under unfavourable conditions. Fertilisation increased maize yield from 1.3–2.6 t ha−1 but the trend in the woodlot effects on maize remained unaltered.  相似文献   

14.
Biophysical models to simulate crop yield are increasingly applied in regional climate impact assessments. When performing large-area simulations, there is often a paucity of data to spatially represent changes in genotype (G) and management (M) across different environments (E). The importance of this uncertainty source in simulation results is currently unclear. In this study, we used a variance-based sensitivity analysis to quantify the relative contribution of maize hybrid (i.e. G) and sowing date (i.e. M) to the variability in biomass yield (YT, total above-ground biomass) and harvest index (HI, fraction of grain in total yield) of irrigated silage maize, across the extent of arable lands in New Zealand (i.e. E). Using a locally calibrated crop model (APSIM-maize), 25 G x M scenarios were simulated at a 5 arc minute resolution (∼5 km grid cell) using 30 years of historical weather data. Our results indicate that the impact of limited knowledge on G and M parameters depends on E and differs between model outputs. Specifically, the sensitivity of YT and HI to genotype and sowing date combinations showed different patterns across locations. The absolute impact of G and M factors was consistently greater in the colder southern regions of New Zealand. However, the relative share of total variability explained by each factor, the sensitivity index (Si), showed distinct spatial patterns for the two output variables. The YT was more sensitive than HI in the warmer northern regions where absolute variability was the smallest. These patterns were characterised by a systematic response of Si to environmental drivers. For example, the sensitivity of YT and HI to hybrid maturity consistently increased with temperature. For the irrigated conditions assumed in our study, inter-annual weather conditions explained a higher share of total variability in the southern colder regions. Our results suggest that the development of methods and datasets to more accurately represent spatio-temporal G and M variability can reduce uncertainty in regional modelling assessments at different degrees, depending on prevailing environmental conditions and the output variable of interest.  相似文献   

15.
Long-term field measured yield data provides good opportunity to assess the impacts of climate and management on crop production. This study used the yield results from a long-term field experiment (1979–2012) at Luancheng Experimental Station in the central part of the North China Plain (NCP) to analyze the seasonal yield variation of winter wheat (Triticum aestivum L.) under the condition of sufficient water supply. The yield change of winter wheat over the last 33 growing seasons was divided into three time periods: the 1980s, the 1990s, and the years of 2001–2012. The grain yield of winter wheat during the 1980s was relative stable. During the 1990s, the annual yield of this crop was continuously increased by 193 kg/ha/year (P < 0.01). While for the past 12 years, yield of winter wheat was maintained at relative higher level, but with larger seasonal yield variation than that back in 1980s. CERES-Wheat model was calibrated and was used to verify the effects of management practices on grain yield. Seven scenarios were simulated with and without improvements in management. The simulated results show that the yield of winter wheat was decreased by 5.3% during 1990s and by 9.2% during the recent 12 seasons, compared with that during 1980s, under the scenario that the yield of winter wheat was solely affected by weather. Seasonal yield variation caused by weather factors was around −39% to 20%, indicating the great effects of weather on yearly yield variation. Yield improvement by cultivars was around 24.7% during 1990s and 52.0% during the recent 12 seasons compared with that during 1980s. The yield improvement by the increase in soil fertility and chemical fertilizer input was 7.4% and 6.8% during the two periods, respectively. The initial higher soil fertility and chemical fertilizer input might be the reasons that the responses of crop production to the further increase in chemical fertilizer were small during the simulation period. Correlation analysis of the grain yield from the field measured data with weather factors showed that sunshine hours and diurnal temperature difference (DTR) were positively, and relative humidity was negatively related to grain yield of winter wheat. The climatic change trends in this area showed that the DTR and sunshine hours were declining. This type of climatic change trend might further negatively affect winter wheat production in the future.  相似文献   

16.
Climate change may lead to an increase in both day and night time temperatures in rice (Oryza sativa L) growing regions, but the impact of such temperature increases on yields of Australian rice varieties is not known. We evaluated the biomass and grain yield response of eleven Australian rice varieties including long, medium and short grain types, and the Californian cultivar M205, to heat stress during the reproductive phase and grain filling stages. Heat stress (day/night = 35/25°C) was applied at one of three stages: from panicle exertion to anthesis (PE), from anthesis to 10 days after anthesis (EGF) and from 10–20 days after anthesis (LGF) periods after which the effect on biomass and grain yield was compared to control plants. When heat stress was applied at PE and early grain filling stages, mean grain yield losses across rice varieties were 83% and 53%, respectively, though significant genotype × heat stress treatment interactions were observed. Notably, three varieties—YRM 67, Koshihikari and Opus—appeared to possess greater tolerance to heat stress at these growth stages. A significant genotype × heat stress treatment interaction was also observed in the LGF treatment, where significant yield reductions were only observed in Opus (21% loss) and YRM 67 (25% loss). A lack of effect of heat stress on total grain yield in most varieties at late grain filling appeared to be due to late tiller grain yields which were either unaffected by the heat stress or increased significantly compared to control plants. While genetic variation for tolerance to heat stress across the three growth stages was observed, there was no rice genotype that was consistently tolerant (in terms of yield under stress) across all three heat stress treatments. In the absence of a genotype that showed broad heat stress tolerance during reproductive growth, we suggest screening of a wider pool of more diverse rice germplasm is warranted.  相似文献   

17.
CERES-Rice模型在中国主要水稻生态区的模拟及其检验   总被引：20，自引：0，他引：20  
本文利用水稻生长观测资料和气象资料,采用CERES-Rice模型在中国主要稻区开展应用研究,对水稻产量、开花期和生物量等的模拟能力进行了评价。所选试验站点分布在不同的水稻生态区,地跨北纬20º02´(海南海口)至北纬45º45´（黑龙江五常）。结果表明,在不同的水稻生态区,CERES-Rice模型模拟水稻开花期  相似文献   

18.
Matching phenology with prevalent abiotic and biotic conditions is a prerequisite for varietal adaptation to the environment. That is particularly important in the context of climate change because an increase in temperature is most likely to modify the precocity of the varieties. The forecast of flowering time in photoperiod‐sensitive sorghum is complex as flowering depends on temperature, day length and soil fertility. The objectives of this work were to quantify effects of latitude on the development of selected sorghum varieties and to verify the precision of our models to predict sorghum maturity. A field experiment at three locations along the latitudinal gradient in Mali with staggered sowing dates (SDs) was conducted. Seven sorghum cultivars covering a wide range of the diversity of cultivated sorghums in Mali were sown on the 10 of June, July and August in 2009 and 2010. Duration of the vegetative phase strongly decreased with latitude. Although the maximum day length difference between locations was < 8 min, for some varieties, we observed a reduction in crop duration of up to 3 weeks. Some varieties were photoperiod insensitive at one location but became photoperiod sensitive at another. The effect of latitude on the phenology is underestimated by the existing models. To determine the optimal areas for the varieties in West Africa and to forecast the effects of climate change, a correction of the simulation coefficients taking account of latitude is proposed. But, in the end, it will be necessary to develop a new model that will be able to predict the effects of both SD and latitude. More research is needed to understand physiological response mechanisms of the pronounced latitude effects on sorghum phenology.  相似文献   

19.
以直接研究观测到气候变化对作物发育和产量的影响,可以为评估气候变化对作物生产影响提供更准确的信息。利用西北地区特干旱（敦煌）、干旱（武威）、半干旱（定西）、半湿润（临夏）、湿润（岷县）5个案例区1981(1986)—2017年地面观测数据,分析气象变化趋势,确定春小麦生长、产量与气候因子的关系。结果表明,1981—2017年间,5个案例区的气候变化模式及其对春小麦物候和产量的影响在空间和时间上是不同的。除极端干旱地区出现较暖和较潮湿的趋势,其他地区观测到较暖和较干燥的趋势。相关分析表明,1981(1986)—2017年武威、定西、临夏站春小麦产量呈增加趋势,但变化趋势除武威站外均不显著,其中武威站生育期内≥30℃天数的减少致使武威站近37年来产量增加,而定西站生育期内降水增多、每穗粒数显著增多及不孕小穗数的显著减少致使定西站近32年产量呈增加趋势。预计随着全球气温的持续升高和未来降水格局的变化,将进一步影响中国西北地区春小麦生产。  相似文献   

20.
Multi-environment trials represent a highly valuable tool for the identification of the genetic bases of crop yield potential and stress adaptation. A Diversity Array Technology®-based barley map has been developed in the ‘Nure’ × ‘Tremois’ biparental Doubled Haploid population, harbouring the genomic position of a gene set with a putative role in the regulation of flowering time and abiotic stress response in barley. The population has been evaluated in eighteen location-by-year combinations across the Mediterranean basin. QTL mapping identified several genomic regions responsible for barley adaptation to Mediterranean conditions in terms of phenology, grain yield and yield component traits. The most frequently detected yield QTL had the early flowering HvCEN_EPS2 locus (chromosome 2H) as peak marker, showing a positive effect from the early winter parent ‘Nure’ in eight field trials, and explaining up to 45.8 % of the observed variance for grain yield. The HvBM5A_VRN-H1 locus on chromosome 5H and the genomic region possibly corresponding to PPD-H2 on chromosome 1H were significantly associated to grain yield in five and three locations, respectively. Environment-specific QTLs for grain yield, and clusters of yield component QTLs not related to phenology and or developmental genes (e.g. on chromosome 4H, BIN_09) were observed as well. The results of this work provide a valuable source of knowledge and tools for both explaining the genetic bases of barley yield adaptation across the Mediterranean basin, and using QTL-associated markers for MAS pre-breeding and breeding programmes.  相似文献   

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