Yield Loss In Winter Wheat Research Articles

An assessment of the relationship between spring frost indicators and global crop yield losses

Reports on the influences of spring frost on crop losses are not consistent, which may be because insufficient indicators of spring frost were included in the analysis. To bridge this gap, we analyzed global temperature datasets and production data for the three major crops of maize, winter wheat, and rice from 1981 to 2016. Five indicators of spring frost events: temperature fluctuation (Tv), temperature difference (Td), duration (Thour), occurrence date (Tdate), and frequency (Tnum) were considered to assess their relationship with yield losses. Linear regression was employed to analyze the change trends in five indicators and random forest was utilized to investigate the relationship between yield loss and indicators of spring frost. Our findings reveal that, despite a decline in the number of spring frost events during global warming, not all the five indicators declined over time. Tv is the most important indicator for yield losses in maize and winter wheat, which shows an increasing trend in their growing regions and provides an explanation for the increasing yield losses of maize and winter wheat over time. Td is the most important indicator of rice yield losses but it shows a decreasing trend in rice-growing areas, which explains why rice yield losses from spring frosts in recent years are not significant.

Effects of climate change and deep fertilization on the growth and yield of winter wheat in the Loess Plateau of China.

Global temperature is projected to rise continuously under climate change, negatively impacting the growth and yield of winter wheat. Optimizing traditional agricultural measures is necessary to mitigate potential winter wheat yield losses caused by future climate change. This study aims to explore the variations in winter wheat growth and yield on the Loess Plateau of China under future climate change, identify the key meteorological factors affecting winter wheat growth and yield, and analyze the differences in winter wheat yield and root characteristics under different fertilization depths. Meteorological data from 20 General Circulation Models were applied to drive the Decision Support System for Agrotechnology Transfer model, simulating the future growth characteristics of winter wheat under various fertilization depths. The Random Forest model was used to determine the relative importance of meteorological factors influencing winter wheat yield, root length density and leaf area index. The results showed that temperature and high emission concentration were primary factors influencing crop yield under future climate change. The temperature increase projected from 2021 to 2100 would be anticipated to shorten the phenology period of winter wheat by 2-16 days and reduce grain yield by 2.9-12.7% compared to the period from 1981 to 2020. Conversely, the root length density and root weight of winter wheat would increase by 1.2-10.9% and 0.2-24.1%, respectively, in the future, and excessive allocation of root system resources was identified as a key factor contributing to the reduction in winter wheat yield. Compared with the shallow fertilization treatment (N5), the deep fertilization treatments (N15 and N25) increased the proportion of roots in the deep soil layer (30-60 cm) by 2.7-10.2%. Because of the improvement in root structure, the decline in winter wheat yield under deep fertilization treatments in the future is expected to be reduced by 1.2% to 6.5%, whereas water use efficiency increases by 1.1% to 2.4% compared to the shallow fertilization treatment. The deep fertilization treatment can enhance the root structure of winter wheat and increase the proportion of roots in the deep soil layer, thereby effectively mitigating the decline in winter wheat yield under future climate change. Overall, optimizing fertilization depth effectively addresses the reduced winter wheat yield risks and agricultural production challenges under future climate change. © 2024 Society of Chemical Industry.

The mechanisms behind the contrasting responses to waterlogging in black-grass (Alopecurus myosuroides) and wheat (Triticum aestivum).

Black-grass (Alopecurus myosuroides ) is one of the most problematic agricultural weeds of Western Europe, causing significant yield losses in winter wheat (Triticum aestivum ) and other crops through competition for space and resources. Previous studies link black-grass patches to water-retaining soils, yet its specific adaptations to these conditions remain unclear. We designed pot-based waterlogging experiments to compare 13 biotypes of black-grass and six cultivars of wheat. These showed that wheat roots induced aerenchyma when waterlogged whereas aerenchyma-like structures were constitutively present in black-grass. Aerial biomass of waterlogged wheat was smaller, whereas waterlogged black-grass was similar or larger. Variability in waterlogging responses within and between these species was correlated with transcriptomic and metabolomic changes in leaves of control or waterlogged plants. In wheat, transcripts associated with regulation and utilisation of phosphate compounds were upregulated and sugars and amino acids concentrations were increased. Black-grass biotypes showed limited molecular responses to waterlogging. Some black-grass amino acids were decreased and one transcript commonly upregulated was previously identified in screens for genes underpinning metabolism-based resistance to herbicides. Our findings provide insights into the different waterlogging tolerances of these species and may help to explain the previously observed patchiness of this weed's distribution in wheat fields.

Impact of irrigation on vulnerability of winter wheat under extreme climate change scenario: a case study of North China Plain

An inadequate understanding of the impacts of adaptation countermeasures tends to exaggerate the adverse effects of climate change on agricultural systems. Motivated by proposing reasonable climate change adaptation countermeasures, the present study applied the EPIC model to quantify the impacts of climate change and irrigation changes with future socioeconomic development on agricultural production. Winter wheat yield losses using dynamic irrigation parameters in the North China Plain (NCP) from 2010 to 2099 under a scenario coupling climate change and future socioeconomic development (RCP8.5-SSP3), and those under an extreme climate change scenario (RCP8.5), were simulated. Results show that EPIC model demonstrates superior performance in simulating winter wheat yields in NCP (RMSE = 12.79 kg/ha), with the distribution of simulated and observed yields is relatively consistent. The winter wheat yield loss in the NCP was high in the south and low in the north. The yield loss rate of winter wheat was 0.21 under the RCP8.5-SSP3 scenario, compared with 0.35 under the RCP8.5 scenario, indicating a superior climatic adaptation of irrigation. However, under the RCP8.5-SSP3 scenario, the yield loss rate increased from 0.17 in the near term to 0.26 in the long term, implying the benefits of irrigation will be diminished with long-term climate change. It is noteworthy that yield improvement was facilitated by irrigation in part of the NCP (accounting for 14.6% area), suggesting that irrigation may lead to an increase in winter wheat yields in some regions even under extreme climate change conditions. This study highlights the significance of quantitatively revealing the benefits and limitations of adaptive countermeasures which could assist in enhancing climate change adaptation while preserving a sustainable agricultural system.

Estimating the Yield Loss of Winter Wheat from Drought in the United States Southern Plains Region as Influenced by El Niño-Southern Oscillation (ENSO)

Estimating the Yield Loss of Winter Wheat from Drought in the United States Southern Plains Region as Influenced by El Niño-Southern Oscillation (ENSO)

Rebuilding high-quality near-surface ozone data based on the combination of WRF-Chem model with a machine learning method to better estimate its impact on crop yields in the Beijing-Tianjin-Hebei region from 2014 to 2019

In recent years, the problem of surface ozone pollution in China has been of great concern. According to observation data from monitoring stations, the concentration of near-surface ozone (O3) in China has gradually increased in recent years, and ozone concentration often exceeds the contaminant limit standard, especially in the Beijing-Tianjin-Hebei (BTH) region. High O3 concentration pollution will adversely affect crop growth, which can cause crop yield losses. Therefore, it is urgent to recognize the situation of ozone pollution in the BTH region and quantitatively evaluate the crop yield losses caused by ozone pollution to develop more effective pollution prevention and control policies. However, the monitoring of ozone concentration in China started relatively late compared with some developed countries, and currently, long-time series data covering the BTH region cannot be obtained, which makes it difficult to evaluate the impact of ozone on crop yield. Therefore, a new method (WRFC-XGB) was proposed in this study to establish a high-precision near-surface O3 concentration dataset covering the whole BTH region from 2014 to 2019 by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) machine learning algorithm. Through verification with ground observation station data, the results of WRFC-XGB are satisfactory, and R2 can reach 0.78–0.91. Compared with other algorithms, the accuracy of the near-surface ozone concentration dataset is greatly improved, which can be used to estimate the impact of surface ozone on crop yield. Based on this dataset, the yield loss of winter wheat, rice, and maize caused by O3 pollution was estimated by using the response equation of the relative yield and ozone dose index. The results showed that the total yield losses of winter wheat, rice and maize from 2014 to 2019 were 2659.21 million tons, 49.23 million tons and 1721.56 million tons due to ozone pollution in the BTH region, respectively, and the highest relative yield loss of crops caused by O3 pollution could be 29.37% during 2014–2019, which indicated that the impact of ozone pollution on crop yield cannot be ignored, and effective measures need to be developed to control ozone pollution, prevent crop production loss, and ensure people's food security.

Exploring dynamic response of agrometeorological droughts towards winter wheat yield loss risk using machine learning approach at a regional scale in Pakistan

ContextCrop yield is a major agriculture sector affected by climate change; especially agrometeorological droughts experienced by south Asian countries in past decades. Research objectiveThe main goals of this research were to explore the spatiotemporal characteristics of seven agrometeorological drought indices at a regional scale in Pakistan. Secondly, to forecast the wheat yield loss risk (YLR) due to droughts under current and future climate scenarios by employing three machine learning (ML) methods; random forest (RF), gradient boosting machine (GBM), and generalized additive model (GAM). MethodThe relationship between detrended wheat yield and a combination of five remote sensing indices Normalized Difference Water Index (NDWI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and Drought Severity Index (DSI)), and two meteorological drought indices (Palmer’s Drought Severity Index (PDSI), and Standardized Precipitation Evapotranspiration Index (SPEI)) was analyzed. Mann-Kendall trend (MK), Sens’s slope, and Sequential Mann-Kendall (SQMK) tests are applied to explore the trend and trend-changing years for all indices over the historical time of 20 years.The YLR and all indices were projected (2021–2050) from the baseline period (2001–2020) using PROPHET time series forecasting and CMIP6 climatic models. YLR was forecasted on present and future projected time series by employing three non-linear ML regression models. ResultsThe output of the drought analysis revealed that the study area was hit by high to severe drought events in 2001–2004, 2006, 2008, 2010, 2012, and 2017. Trend analysis revealed intersection years breaking the rising trend of drought indices. All drought indices are significantly correlated with meteorological wheat yield with a sequence of NDWI>DSI>VCI>VHI>PDSI>SPEI>TCI. Future projections under high emission scenarios revealed a rise in YLR associated with frequent projected droughts from VHI, DSI, SPEI, and PDSI. YLR forecasting from agrometeorological indices is best predicted by random forest with the lowest RMSE = 0.005314. NDWI (26%) and VCI (19%) are found to be significant relative predictors associated with 51% high YLR in the baseline period and SPEI (20%) and NDWI (17%) as the most important relative predictors associated with 39% high YLR in future. ConclusionThe region is vulnerable to agrometeorological droughts with more susceptibility to less rain and high temperature affecting crop health and a high risk of yield loss in the future. ImplicationThe study provides a direction to stakeholders and policymakers to develop and adapt better strategies to mitigate and prevent drought-related yield loss risk in the future.

Effects of Climate Change on Wheat Yield and Nitrogen Losses per Unit of Yield in the Middle and Lower Reaches of the Yangtze River in China

Nitrogen fertilizer is one of the essential nutrients for wheat growth and development, and it plays an important role in increasing and stabilizing wheat yield. Future climate change will affect wheat growth, development, and yield, since climate change will also alter nitrogen cycles in farmland. Therefore, further research is needed to understand the response of wheat yield and nitrogen losses to climate change during cultivation. In this study, we investigate the wheat-producing region in the middle and lower reaches of the Yangtze River in China, one of the leading wheat-producing areas, by employing a random forest model using wheat yield records from agricultural meteorological observation stations and spatial data on wheat yield, nitrogen application rate, and nitrogen losses. The model predicts winter wheat yield and nitrogen losses in the middle and lower reaches of the Yangtze River based on CMIP6 meteorological data and related environmental variables, under SSP126 and SSP585 emission scenarios. The results show that future climate change (temperature and precipitation changes) will decrease winter wheat yield by 2~4% and reduce total nitrogen losses by 0~5%, but in other areas, the total nitrogen losses will increase by 0~5% and the N leaching losses per unit of yield will increase by 0~10%. The results of this study can provide a theoretical basis and reference for optimizing nitrogen application rates, increasing yield, and reducing nitrogen losses in wheat cultivation under climate change conditions.

Control of Multiple-Herbicide-Resistant Canada Fleabane With Fall, Spring, and Sequential Herbicide Applications in Winter Wheat

Limited information exists on the efficacy of pyrasulfotole/bromoxynil, fluroxypyr/halauxifen-methyl + MCPA EHE, and clopyralid applied in the fall, spring, or sequentially [fall followed by (fb) spring] for the control of multiple-herbicide-resistant (MHR) Canada fleabane in winter wheat under Ontario environmental conditions. Three field experiments were initiated in the autumn of 2020 and 2021 for a total of 6 site-years to evaluate fall- and spring-applied herbicides and their sequential applications for the control of MHR Canada fleabane in winter wheat in Ontario. Pyrasulfotole/bromoxynil applied in the fall, spring, or sequentially controlled MHR Canada fleabane 83, 99, and 100%, respectively at 8 weeks after the spring application (WAB); the spring and sequential applications provided better control than the fall application. Fluroxypyr/halauxifen + MCPA and clopyralid applied in the fall, spring, or sequentially controlled MHR Canada fleabane 97 to 100% and 99 to 100%, respectively at 8 WAB. Based on orthogonal contrasts the spring and sequential herbicide applications provided greater control than the fall application (8 WAB). MHR Canada fleabane interference reduced winter wheat yield up to 27% in this study. Based on orthogonal contrasts reduced MHR Canada fleabane interference with the fall application resulted in 17% higher winter wheat yield than when herbicide application was delayed to the spring. Although MHR Canada fleabane was controlled very effectively with clopyralid winter wheat yield was lower, presumably due to crop injury; this observation will have to be explored further in future research. Results from this study indicate that pyrasulfotole/bromoxynil and fluroxypyr/halauxifen + MCPA applied in the fall can be used to effectively control MHR Canada fleabane and minimize winter wheat yield loss due to weed interference.

Assessment of photosynthesis and yield loss of winter wheat under ground-level ozone exposure

Assessment of photosynthesis and yield loss of winter wheat under ground-level ozone exposure

Hyperspectral estimation of wheat stripe rust using fractional order differential equations and Gaussian process methods

Wheat stripe rust is the main cause of yield loss in winter wheat. For nondestructive monitoring of wheat stripe rust by remote sensing, a high-precision stripe rust monitoring model can be constructed so that field management can be performed rationally and environmental damage from large doses of pesticides and chemicals can be avoided. Fractional order differential (FOD) equations can be used to flexibly control the differential step size, which enhances the spectral information and reduces the background noise. In this study, the canopy hyperspectral data under the influence of wheat stripe rust were evaluated by FOD, and the disease severity level (SL) of stripe rust in the field was analyzed by evaluating the polar difference, coefficient of variation, and correlation coefficient. Subsequently, the spectral features associated with stripe rust were screened using significance tests and Gaussian process regression sigma (GPR sigma) analysis methods. Then, models for the various wheat stripe rust severity levels were established by Gaussian process regression (GPR) with different data inputs. The results showed that the 0.8–1.4 differential order could effectively improve the correlation between spectral bands and disease severity, and the optimal correlation between the 1.2 order differential spectra and wheat stripe rust severity improved by 15% compared with the original reflectance spectra. The GPR sigma band analysis method screens only 8 bands at order 1.2, the R2 between the model-predicted SL and the measured SL is improved by 13% compared to the original reflectance spectrum, and the RMSE and MAE are reduced by 34% and 39%, respectively. Compared with the significance test method, GPR sigma band analysis selected fewer bands, constructed models with higher accuracy and was more suitable for the construction of models for estimating the severity of wheat stripe rust disease.

U.S. winter wheat yield loss attributed to compound hot-dry-windy events

Climate extremes cause significant winter wheat yield loss and can cause much greater impacts than single extremes in isolation when multiple extremes occur simultaneously. Here we show that compound hot-dry-windy events (HDW) significantly increased in the U.S. Great Plains from 1982 to 2020. These HDW events were the most impactful drivers for wheat yield loss, accounting for a 4% yield reduction per 10 h of HDW during heading to maturity. Current HDW trends are associated with yield reduction rates of up to 0.09 t ha−1 per decade and HDW variations are atmospheric-bridged with the Pacific Decadal Oscillation. We quantify the “yield shock”, which is spatially distributed, with the losses in severely HDW-affected areas, presumably the same areas affected by the Dust Bowl of the 1930s. Our findings indicate that compound HDW, which traditional risk assessments overlooked, have significant implications for the U.S. winter wheat production and beyond.

Urea amendment alleviated morphological and physiological damages and yield loss of winter wheat subjected to low temperature stress at jointing stage

Urea amendment alleviated morphological and physiological damages and yield loss of winter wheat subjected to low temperature stress at jointing stage

Predicting yield loss in winter wheat due to frost damage during stem elongation in the central area of Huang-huai plain in China

Natural frost during stem elongation of winter wheat is one of the most destructive weather-related events in the Huang-Huai plain of China. Early prediction for yield loss helps to guide the timely implementation of a post-frost management strategy. Currently, frost stress indices calculated using the minimum Stevenson screen temperature (ST) only consider the effects of low temperature and duration of frost damage, exhibiting a limited ability for the early prediction of yield loss. Therefore, this study aimed to propose a new index to improve the accuracy of early prediction for yield loss. In this study, Shangqiu was selected for a survey during 2015–2019 where we proposed a method to calculate the percent yield difference (PYD) based on samples of wheat collected during the reproductive stage. In addition, we considered the impact of the compensation of regenerated tillers on PYD. An integrated frost stress (IFS) index was proposed based on the hourly minimum grass temperature (GT) at the regional scale. The IFS index integrated the influence of low temperature, duration, and developmental progress of winter wheat on frost damage and was compared with two accumulated frost degree-days (AFDD) indices to predict PYD. Frost reduced the grain number per ear and grain yield significantly ( p < 0.05), and reduced ear number and 1000-grain weight to a lesser extent. The average PYD reached 18.3% in 2018, followed by 11.2% in 2015, 4.2% in 2016, and 1.8% in 2017. Regenerated tillers contributed to yield only if the PYD increased to a certain extent. Compensation by regenerated tillers for PYD in 2018 was only 2.8% and 0.4% in the experimental field and non-experimental fields, respectively, without significant impact ( p > 0.05) on the PYD. Compared with the AFDD indices, the IFS index improved the accuracy of early prediction for PYD significantly, with the lowest root mean square error (RMSE) of 6.4%, which showed the advantage of considering the developmental progress and the hourly GT. The IFS, which adopted a critical temperature of − 3 ℃, produced the best calibration model, with the highest interpretation rate of 69.5% for variation in PYD. Although the calibration model did not show the highest accuracy in the validation, its fitted line was closest to the 1:1 line, which indicated the smallest deviation of predicted PYD from measured PYD. This research demonstrated the potential of the IFS index calculated by hourly GT data to evaluate frost damage to winter wheat during stem elongation, which will help guide site-specific, post-frost management. • An integrated frost stress (IFS) index based on hourly minimum grass temperature was proposed. • The IFS integrated low temperature, frost duration, and developmental progress of winter wheat. • The IFS with a temperature of − 3 ℃ yielded the best accuracy for predicting percent yield difference.

The relationship between Fusarium head blight traits, thousand-kernel weight, and yield in winter wheat

Factors influencing Fusarium head blight (FHB) occurrence and yield losses in winter wheat have extensively been studied through the years; however, reports on the relationship between FHB traits and yield are conflicting. In addition, studies neglected the relationship between FHB traits and thousand-kernel weight (TKW). This study evaluated the variability in the relationship between FHB traits, TKW, and yield under field conditions, using 40 commercial winter wheat cultivars differing in resistance/susceptibility to FHB. In general, the FHB index had greater relation to yield, while Fusarium-damaged kernels (FDK) was more related to TKW. The relationship between yield, TKW, FHB index, and FDK was also determined by resistance/susceptibility of winter wheat cultivars. Fusarium-damaged kernels influenced yield more in moderately resistant (R2 = 43 %) than in susceptible/moderately susceptible cultivars (R2 = 27 %). The influence of the FHB index on TKW was weak (R2 = 9 %) in susceptible/moderately susceptible cultivars and in moderately resistant ones (R2 = 1 %). The potential to predict TKW from yield under pathogen pressure was limited due to the moderate-to-positive correlation between yield and TKW (r = 0.349, p < 0.001). This study provides insights into factors that influence TKW under FHB pathogen pressure and gives direction to more efficient and reliable investigations on grain resistance toward FHB.

Potential wheat yield loss due to weeds in the United States and Canada

AbstractYield losses due to weeds are a major threat to wheat production and economic well-being of farmers in the United States and Canada. The objective of this Weed Science Society of America (WSSA) Weed Loss Committee report is to provide estimates of wheat yield and economic losses due to weeds. Weed scientists provided both weedy (best management practices but no weed control practices) and weed-free (best management practices providing &gt;90% weed control) average yield from replicated research trials in both winter and spring wheat from 2007 to 2017. Winter wheat yield loss estimates ranged from 2.9% to 34.4%, with a weighted average (by production) of 25.6% for the United States, 2.9% for Canada, and 23.4% combined. Based on these yield loss estimates and total production, the potential winter wheat loss due to weeds is 10.5, 0.09, and 10.5 billion kg with a potential loss in value of US$2.19, US$0.19, and US$2.19 billion for the United States, Canada, and combined, respectively. Spring wheat yield loss estimates ranged from 7.9% to 47.0%, with a weighted average (by production) of 33.2% for the United States, 8.0% for Canada, and 19.5% combined. Based on this yield loss estimate and total production, the potential spring wheat loss is 4.8, 1.6, and 6.6 billion kg with a potential loss in value of US$1.14, US$0.37, and US$1.39 billion for the United States, Canada, and combined, respectively. Yield loss in this analysis is greater than some previous estimates, likely indicating an increasing threat from weeds. Climate is affecting yield loss in winter wheat in the Pacific Northwest, with percent yield loss being highest in wheat-fallow systems that receive less than 30 cm of annual precipitation. Continued investment in weed science research for wheat is critical for continued yield protection.

Drought characterization across agricultural regions of China using standardized precipitation and vegetation water supply indices

Understanding the spatiotemporal dynamics of drought and its potential effect on crop production is critical in decision-making processes to support sustainable food production under climate change. This is especially true for China, the largest producer of cereal crops in the world, where production losses could significantly impact the global food chain. Here, we utilized global remote sensing products to study the spatiotemporal dynamics of meteorological and agricultural droughts across all of China during 1982–2018 and their potential linkage with winter wheat and summer corn yield losses since 2000. Specifically, we derived the agricultural Standardized Precipitation Index (aSPI; at 1, 3, and 6-month scales) and the Standardized Vegetation Supply Water Index (SVSWI) and explored their regional-scale dynamics across four sub-regions of China. Results suggest that the frequency of extreme drought events has increased in China since 2000, mostly affecting the winter wheat growing season (i.e., December–May) across North China. This increased frequency of drought events followed consistent spatiotemporal patterns of 3-month aSPI, mostly affecting Northwest/North China, Qinghai-Tibet, and South China during April, December, and May, respectively. The 3-month aSPI and SVSWI anomalies showed a good correlation with winter wheat yield anomaly across the Qinghai-Tibet area and corn yield across South China. The summer corn yield across all sub-regions and winter wheat yield across some agricultural zones in North China showed significant decreasing trends since 2000. This study provides a useful understanding of the spatiotemporal dynamics of agricultural drought and its potential implications for crop production in China.

Accuracy Assessment of the SPEI, RDI and SPI Drought Indices in Regions of Iran with Different Climate Conditions

Considering the impact of drought on agricultural products and human food security, the selection of the appropriate drought index to assess drought conditions is very important. Therefore, in this research, based on the relationship between the percent annual yield loss (AYL) of winter wheat (Triticum sativum) and three commonly used drought indices, i.e. Standardized Precipitation Evapotranspiration Index (SPEI), Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI), the accuracy of these indices was evaluated at 1-, 3-, 6- and 12-month time scales. Results showed that the average AYL at Ahvaz, Babolsar, Esfahan, Gorgan, Kerman, Mashhad, Ramsar, Rasht, Shiraz and Zabol was 66.64, 5.42, 97.52, 10.20, 98.57, 84.99, 2.47, 3.84, 77.03 and 97.07%, respectively. At stations with hyper-arid, semi-arid, Mediterranean, humid and hyper-humid type A climate conditions such as Zabol, Esfahan, Mashhad, Shiraz, Gorgan, Babolsar, Ramsar and Rasht stations, the calculated values of SPEI demonstrated the highest CC with AYL in winter wheat. At Kerman and Ahvaz, with arid climate conditions, the calculated values of the RDI had the greatest CC with AYL in winter wheat. Thus, in general, among the SPI, RDI and SPEI, the use of the SPEI is recommended for the assessment of drought characteristics.

Productivity and water use in intensified forage soybean-wheat cropping systems of the US southern Great Plains

Agro-ecological intensification of continuous dryland wheat ( Triticum aestivum L.) systems with forage soybean [ Glycine max (L.) Merr] in lieu of summer fallow could provide nutritious summer forage for livestock, reduce requirements for inorganic N fertilizer for winter wheat, and ameliorate sustainability issues associated with summer fallow, such as soil erosion and low precipitation use efficiency ( PUE ). This modeling study utilized DSSAT-CSM to assess: the yield and water use of three cultivars of forage soybean of diverse maturity groups (MGs) harvested at three different dates [60, 90, and 120 days after planting (DAP)]; and their influence on performance and water productivity of subsequent winter wheat, and overall double-cropping systems in comparison to the fallow-wheat system. Crop yield and evapotranspiration (ET) data, collected from continuous field experiments at El Reno, Oklahoma (35.57 °N, 98.03 °W) during 2002–2006, were used for calibration and validation of soybean and wheat models. Long-term simulations conducted using historical weather data (1994–2019) suggested that mid-maturity group (MGV) soybean could provide greater forage yields and higher water use efficiency ( WUE B ) than later-maturing soybeans (MGVI & MGVII) when harvested at 90–120 days after planting (DAP). Double-cropping forage soybean and winter wheat caused a reduction of 1.4-2.1 Mg ha −1 grain and 4.7-5.6 Mg ha −1 and biomass by wheat, and higher seasonal ET losses of 77−132 mm than the fallow-wheat system. However, trade-offs in yields between forage soybean and winter wheat were effective when precipitation was >180 mm during the first 60 d of growth by soybean. Despite yield losses in winter wheat, double-cropped forage soybean-wheat systems could be a viable option based on its economic competitiveness and other ecological benefits.

Effects of O3 pollution near formation on crop yield and economic loss

Abstract With the rapid development of industrialization and urbanization, a large number of ozone (O3) precursors such as nitrogen oxides and volatile organic compounds increase, which leads to the aggravation of O3 pollution in the atmosphere near the formation, which has an important impact and great harm on food crops, cash crops and natural vegetation. This paper aims to establish a regional evaluation method of O3 on winter wheat yield loss, reveal the status of winter wheat yield loss caused by O3 pollution, and propose important scientific countermeasures for food security and air pollution control. Based on the impact on the winter wheat field in the research of open top chamber type test data, and at the same time the surface O3 observation data and related meteorological data, detailed analysis of the surface of some areas of O3 concentration variation characteristics and its relationship with meteorological elements, analysis to determine the O3 on winter wheat photosynthesis and yield characteristics and dynamic changes of damage threshold, improved the O3 absorption of flux model, and established the O3 cumulated absorb effective dose and the relation model of winter wheat biomass and yield loss, improve the response model based on flux method of evaluation on the effect of O3 on crop yield. The results showed that for every 1 mmol O3 m−2 increase in effective O3 flux absorbed by stomatal accumulation in winter wheat leaves, the loss of ear weight was 5.2% compared with the control group. When the effective O3 flux of stomatal cumulative absorption in winter wheat leaves increased by 1 mmol O3 m−2, the total dry weight loss was 5.3% compared with the control group. When the effective O3 flux of stomatal cumulative absorption in winter wheat leaves increased by 1 mmol O3 m−2, the economic yield loss was 2.8% compared with the control group.