Weeds are notorious yield reducers that are, in many situations, economically more harmful than insects, fungi or other crop pests. Assessment of crop yield and economic losses due to weeds in ...agriculture is an important aspect of study which helps in devising appropriate management strategies against weeds. A study was conducted to estimate the yield and economic losses due to weeds using the data from 1581 On-Farm Research trials conducted by All India Coordinated Research Project on Weed Management between 2003 and 14 in major field crops in different districts of 18 states of India. The study revealed that potential yield losses were high in case of soybean (50–76%) and groundnut (45–71%). Greater variability in potential yield losses were observed among the different locations (states) in case of direct-seeded rice (15–66%) and maize (18–65%). Three factors viz. location (state), crop, and soil type significantly (p < .0001) explained the variability in actual yield losses due to weeds at farmers’ fields. Significant differences were also observed between different locations, crops and soil types. Actual economic losses were high in the case of rice (USD 4420 million) followed by wheat (USD 3376 million) and soybean (USD 1559 million). Thus, total actual economic loss of about USD 11 billion was estimated due to weeds alone in 10 major crops of India viz. groundnut (35.8%), soybean (31.4%), greengram (30.8%), pearlmillet (27.6%), maize (25.3%), sorghum (25.1%), sesame (23.7%), mustard (21.4%), direct-seeded rice (21.4%), wheat (18.6%) and transplanted rice (13.8%).
•Weeds are economically more harmful than other crop pests.•Yield and economic losses due to weeds were estimated in 10 major field crops.•Significant differences were observed between locations, crops and soil types.•Overall economic losses were high in rice followed by wheat and soybean.•Greatest loss of about USD 347/ha was observed in groundnut followed by maize.
In order to tackle China’s severe air pollution issue, the government has released the “Air Pollution Prevention and Control Action Plan” (known simply as the “Action Plan”) since 2013. A recent ...study reported a decreased trend in PM2.5 concentrations over 2013–2017, but O3 pollution has become more serious. However, the effects of surface O3 on crops are unclear after the implementation of the “Action Plan”. Here, we evaluated the potential negative effects of surface O3 on three main food crops (winter wheat, maize and rice) across China during 2015–2018 using nationwide O3 monitoring data and AOT40-yield response functions. Results suggested that mean O3 concentration, AOT40 and relative yield loss in China showed an overall upward trend from 2015 to 2018. During winter wheat, maize, single rice, double-early rice, and double-late rice growing seasons, mean O3 concentration in recent years ranged from 38.6 to 46.9 ppb, 40.2–43.9 ppb, 39.3–42.2 ppb, 33.8–40.0 ppb, and 35.9–39.1 ppb, respectively, and AOT40 mean values ranged from 8.5 to 14.3 ppm h, 10.5–13.4 ppm h, 9.8–11.9 ppm h, 5.2–9.2 ppm h, and 8.0–9.5 ppm h, respectively. O3-induced yield reductions were estimated to range from 20.1 to 33.3% for winter wheat, 5.0–6.3% for maize, 7.3–8.8% for single rice, 3.9–6.8% for double-early rice and 5.9–7.1% for double-late rice. O3-induced production losses for winter wheat, maize, single rice, double-early rice, and double-late rice totaled 39.5–88.2 million metric tons, 12.6–21.0 million metric tons, 9.5–11.3 million metric tons, 1.2–1.8 million metric tons, and 2.2–2.7 million metric tons, respectively, and the corresponding economic losses totaled 14.3–32.0 billion US$, 3.9–6.5 billion US$, 3.9–4.6 billion US$, 0.5–0.7 billion US$, and 0.9–1.1 billion US$, respectively. Our results suggested that the government should take effective measures to reduce O3 pollution and its effects on agricultural production.
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•Surface O3 is posing an increasingly serious threat to main food crops in China.•The O3-induced yield losses were 20.1–33.3% for wheat, 5.0–6.3% for maize, and 3.9–8.8% for rice.•Annual production losses amounted to 65–124 million metric tons, worth 24–45 billion US$.•Some policies are urgently needed to reduce the risks caused by O3 to agricultural production.
The main finding of this study: This study provides an estimate of O3-induced crop yield losses across China after the implementation of the “Action Plan” using nationwide O3 measurements, suggesting a potential risk for agricultural production in recent years.
Tropospheric ozone (O3) is a pollutant of widespread concern in the world and especially in China for its negative effects on agricultural crops. For the first time, yield and economic losses of ...wheat between 2014 and 2017 were estimated for the North China Plain (NCP) using observational hourly O3 data from 312 monitoring stations and exposure-response functions based on AOT40 index (accumulated hourly O3 concentration above 40 ppb) from a Chinese study. AOT40 values from 2014 to 2017 during the wheat growing seasons (75-days, 44 before and 30 after mid-anthesis) ranged from 3.1 to 14.9 ppm h, 4.9–17.5 ppm h, 7.3–17.6 ppm h, and 0.5–18.6 ppm h, respectively. The highest AOT40 values were observed in the Beijing-Tianjin-Hebei region. The values of relative yield losses from 2014 to 2017 were in the ranges of 6.4–30.5%, 10.0–35.8%, 14.9–34.1%, and 21.6–38.2%, respectively. The total wheat production losses in NCP for 2014–2017 accounted for 18.5%, 22.7%, 26.2% and 30.8% in the whole production, while the economic losses amounted to 6,292 million USD, 8,524 million USD, 10,068 million USD, and 12,404 million USD, respectively. The important impact of O3 in this area, which is of global importance, should be considered when assessing wheat yield production. Our results also show an increasing trend in AOT40, relative yield loss, total crop production loss and economic loss in the four consecutive years.
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•Wheat yield loss by ambient O3 was estimated by using observed O3 data.•AOT40, yield and economic losses at county levels were calculated in North China Plain.•Four consecutive years data were used to increase the representative of assessment results.•The value of AOT40 and yield loss showed an increasing trend from 2014 to 2017.•The wheat yield loss due to O3 from 2014 to 2017 accounts for 18.5–30.8%.
Quantifying the impact of thermal stress on milk yields is essential to effectively manage present and future risks in dairy systems. Despite the existence of numerous heat indices designed to ...communicate stress thresholds, little information is available regarding the accuracy of different indices in estimating milk yield losses from both cold and heat stress at large spatio-temporal scales. To address this gap, we comparatively analyzed the performance of existing thermal indices in capturing US milk yield response to both cold and heat stress at the national scale. We selected four commonly used thermal indices: the Temperature and Humidity Index (THI), Black Globe Humidity Index (BGHI), Adjusted Temperature and Humidity Index (THIadj), and Comprehensive Climate Index (CCI). Using a statistical panel regression model with observational and reanalysis weather data from 1981–2020, we systematically compared the patterns of yield sensitivities and statistical performance of the four indices. We found that the US state-level milk yield variability was better explained by the THIadj and CCI, which combine the effects of temperature, humidity, wind, and solar radiation. Our analysis also reveals a continuous and nonlinear responses of milk yields to a range of cold to heat stress across all four indices. This implies that solely relying on fixed thresholds of these indices to model milk yield changes may be insufficient to capture cumulative thermal stress. Cold extremes reduced milk yields comparably to those impacted by heat extremes on the national scale. Additionally, we found large spatial variability in milk yield sensitivities, implying further limitations to the use of fixed thresholds across locations. Moreover, we found decreased yield sensitivity to thermal stress in the most recent two decades, suggesting adaptive changes in management to reduce weather-related risks.
Exposure to ambient ozone (O3) is a risk factor for public health and causes damage to vegetation, including agricultural crops. In this study, we performed a comprehensive estimate of the spatial ...distribution of premature deaths and main crop yield losses attributed to ambient O3, across China in 2014, by applying the Global Burden of Diseases approach and AOT40 metric (i.e., above a threshold of O3 concentration of 40 ppb). Our results show that China's total premature deaths in 2014 due to COPD attributed to O3 exposure were 89,391 (CI95: 32,225–141,649) with spatial variation across provinces. O3 induced production losses from all crops were 78.4 million metric tons, and the relative yield losses ranged from 8.5 to 14.0% for winter wheat, 3.9–15.0% for rice, and 2.2–5.5% for maize. The top four Chinese provinces (Sichuan, Shandong, Henan and Hunan) for premature deaths attributed to O3 pollution also suffered severe losses in yields of winter wheat and rice. Our results provide quantitative evidence of O3 induced impacts on both the public health and crop yields across Chinese provinces, which have important policy implications for the government to alleviate O3 pollution in addition to PM2.5 pollution that is currently being addressed.
•Impacts of O3 on both public health and crop yield were analyzed for China.•Premature deaths attributed to ambient O3 were 89,391 in 2014.•O3 induced reductions of winter wheat, rice and maize reached 78.4 Mt in 2014.•Top provinces of O3 induced health burden also suffered serious crop yield losses.•More attention should be paid to exacerbated O3 impacts in some inner provinces.
Tropospheric ozone (O3), the most important phytotoxic air pollutant, can deteriorate crop quality and productivity. Notably, satellite and ground-level observations-based multimodel simulations ...demonstrate that the present and future predicted O3 exposures could threaten food security. Hence, the present study aims at reviewing the phytotoxicity caused by O3 pollution, which threatens the food security. The present review encompasses three major aspects; wherein the past and prevailing O3 concentrations in various regions were compiled at first, followed by discussing the physiological, biochemical and yield responses of economically important crop species, and considering the potential of O3 protectants to alleviate O3-induced phytotoxicity. Finally, the empirical data reported in the literature were quantitatively analysed to show that O3 causes detrimental effect on physiological traits, photosynthetic pigments, growth and yield attributes. The review on prevailing O3 concentrations over various regions, where economically important crop are grown, and their negative impact would support policy makers to implement air pollution regulations and the scientific community to develop countermeasures against O3 phytotoxicity for maintaining food security.
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The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied ...modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.
•Overview of the current state of development in coupling pest and disease models to crop models•Technical and scientific challenges in coupling pest and disease model to crop models•Roadmap to improve the simulation of the impacts caused by plant diseases and pests
Crop yield is primarily determined by physiological parameters, including light interception by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, little information ...is available about how these physiological parameters are impacted by planting date and what their contributions are to cotton yield loss. To evaluate the relative contribution of each physiological parameter to planting date-associated yield loss in cotton, an experiment was conducted during the 2019 to 2021 growing seasons at a field site in Anyang, Henan, China. Two contemporary cotton cultivars were grown in the field with four different planting dates (PDMAY10, PDMAY20, PDMAY30 and PDJUN10) for the growing seasons from 2019 to 2021. The measurements included climatic parameters, seed cotton yield, yield components, HI and biweekly leaf area index, light interception and biomass. First, seed cotton yield was significantly affected by the planting dates, with yields ranging from 1984 to 3388 kg ha−1, averaged across cultivars and years, while a significant difference between the first three planting dates was observed only in 2021. Second, the planting date significantly impacted the total light interception during the growing season (IPARtotal) and HI, while IPARtotal and HI decreased with the delayed planting date in all the years of the study. Compared to PDMAY10, IPARtotal was 0.34%, 13.2%, and 19.9% lower for PDMAY20, PDMAY30 and PDJUN10, respectively. Averaged across cultivars and years, PDMAY10 resulted in the highest HI value (0.28). The RUE gradually decreased as the planting date was delayed, but a significant difference in RUE between planting dates was observed only in 2021. Overall, among the three physiological yield-driving parameters, IPARtotal was a stronger contributor (46.9–47.0%) to the yield loss for PDJUN10 than PDMAY10 and PDMAY20. However, when compared to PDMAY30, HI was the greatest contributor, accounting for approximately 72.5% of the seed cotton yield loss for PDJUN10, followed by IPAR. We found that when cotton sowing in the Yellow River basin was postponed until June 10, the cotton yield was significantly reduced, and IPAR and HI were the main factors responsible for PDJUN10 yield loss on average. This study identified the most important functional traits for seed cotton yield response to planting date and has important reference value for stable cotton production.
●The contribution made by physiological parameters to variation in cotton yield was quantified.●The yield loss of late planting cotton was mainly due to light interception and the harvest index.●The reduction in light interception was mainly due to cumulative global radiation.
A commonly held misconception is that weeds reduce crop yield primarily because of resource competition.Weed presence reduces crop yield regardless of resource availability based on the timing of the ...critical period for weed control, and resource supplementation, and weed density studies.Weeds alter developmental trajectories of crops early in the growing season that often result in reduced yields.Signals produced by weeds that alter crop growth include light quality alterations, soil-borne chemicals, and/or volatile chemicals.Weed signals elicit a stress response in crops that may suppress growth through repression of the TARGET OF RAPAMYCIN (TOR) signaling system.Identification of weed-inducible genes and promoters will provide tools to investigate mechanisms controlling crop–weed interactions and develop weed-tolerant crops.
Direct competition for resources is generally considered the primary mechanism for weed-induced yield loss. A re-evaluation of physiological evidence suggests weeds initially impact crop growth and development through resource-independent interference. We suggest weed perception by crops induce a shift in crop development, before resources become limited, which ultimately reduce crop yield, even if weeds are subsequently removed. We present the mechanisms by which crops perceive and respond to weeds and discuss the technologies used to identify these mechanisms. These data lead to a fundamental paradigm shift in our understanding of how weeds reduce crop yield and suggest new research directions and opportunities to manipulate or engineer crops and cropping systems to reduce weed-induced yield losses.
In the context of global climate change, droughts pose a serious threat to agricultural development and food security. Assessing the vulnerability and risk of regions to drought is important for its ...prevention. In this paper, to understand the vulnerability of maize to drought in different regions of China and quantify its risk, 241 prefecture-level administrative regions (including prefecture-level cities, autonomous prefectures, prefectures, and leagues) in the five main maize-growing regions of China are used as study area. By using a method of global sensitivity analysis, the extended Fourier amplitude sensitivity test (EFAST), we chose two parameters that are most sensitive to maize yield to calibrate the AquaCrop model. We then used it to simulate the water stress of maize in the study area under different irrigation scenarios as well as the corresponding production. We defined the drought hazard index (DHI) as the daily average of the crop water stress indicator during the growing season, and used it to describe the intensity of droughts. Vulnerability curves (the function of the DHI and rate of yield loss) of the entire growth season and various stages of growth were also formulated. These were used to determine the loss of maize yield under four levels of risk (return periods of 5, 10, 20, and 50 years). The results showed the following: 1) the vulnerability curve of maize for the entire growing season was consistent with logistic function, and the coefficient of determination of the equation of regression was R2 = 0.93. The rate of yield loss began increasing rapidly once the DHI had reached 0.2 and approached its maximum value when the DHI was 0.6. 2) The coefficients of determination of the results of regression in 14 scenarios, in which drought had occurred in different stages of growth, were between 0.28 and 0.92. Drought from the tasseling stage to the milk stage had the most significant negative effect on the maize yield, followed by the seventh leaf stage to the tasseling stage and the sowing stage to the seventh leaf stage. Drought from the milk stage to physiological maturity had the least negative effect on the maize yield. 3) Under all four risk levels, the DHI and the yield loss rate of maize in China decreased from the northwest to the southeast. The Northwest Irrigated Maize Region had the highest drought risk among the five maize-growing regions, followed by the North Spring Maize Region, the Huang-Huai-Hai Summer Maize Region, the South Hilly Maize Region, and the Southwest Mountain Maize Region. 4) The DHI calculated by the average method was more representative than that calculated using the accumulative method.
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•A new drought hazard index was proposed for building drought loss curves.•Drought vulnerability curves were built for five main maize-growing regions in China.•Yield loss curves of maize were built for droughts occurred in various growth stages.•Risk of maize yield loss for return periods of 5, 10, 20, and 50 years were assessed.•The drought risk of maize in China decreases from northwest to southeast.
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