Explore the vast range of titles available.

Surface ozone is mainly produced by photochemical reactions involving various anthropogenic pollutants, whose emissions are increasing rapidly in India due to fast-growing anthropogenic activities. This study estimates the losses of wheat and rice crop yields using surface ozone observations from a group of 17 sites, for the first time, covering different parts of India. We used the mean ozone for 7 h during the day (M7) and accumulated ozone over a threshold of 40 ppbv (AOT40) metrics for the calculation of crop losses for the northern, eastern, western and southern regions of India. Our estimates show the highest annual loss of wheat (about 9 million ton) in the northern India, one of the most polluted regions in India, and that of rice (about 2.6 million ton) in the eastern region. The total all India annual loss of 4.0–14.2 million ton (4.2–15.0%) for wheat and 0.3–6.7 million ton (0.3–6.3%) for rice are estimated. The results show lower crop loss for rice than that of wheat mainly due to lower surface ozone levels during the cropping season after the Indian summer monsoon. These estimates based on a network of observation sites show lower losses than earlier estimates based on limited observations and much lower losses compared to global model estimates. However, these losses are slightly higher compared to a regional model estimate. Further, the results show large differences in the loss rates of both the two crops using the M7 and AOT40 metrics. This study also confirms that AOT40 cannot be fit with a linear relation over the Indian region and suggests for the need of new metrics that are based on factors suitable for this region.

Crop losses from weed interference have a significant effect on net returns for producers. Herein, potential corn yield loss because of weed interference across the primary corn-producing regions of the United States and Canada are documented. Yield-loss estimates were determined from comparative, quantitative observations of corn yields between nontreated and treatments providing greater than 95% weed control in studies conducted from 2007 to 2013. Researchers from each state and province provided data from replicated, small-plot studies from at least 3 and up to 10 individual comparisons per year, which were then averaged within a year, and then averaged over the seven years. The resulting percent yield-loss values were used to determine potential total corn yield loss in t ha−1 and bu acre−1 based on average corn yield for each state or province, as well as corn commodity price for each year as summarized by USDA-NASS (2014) and Statistics Canada (2015). Averaged across the seven years, weed interference in corn in the United States and Canada caused an average of 50% yield loss, which equates to a loss of 148 million tonnes of corn valued at over U.S.$26.7 billion annually. Nomenclature: Corn, Zea mays L.

Extreme climate events can threaten food production and disrupt supply chains. For instance, the 2023 drought in Catalonia caused large areas of winter cereals to wilt and die early, yielding no grain. This study examined whether Sentinel-2 can detect total crop losses of winter cereals using ground truth data on crop failure. The methodology explored which Sentinel-2 phenological and greenness variables could best predict three drought impact classes: normal growth, moderate impact, and high impact, where the crop failed to produce grain. The results demonstrate that winter cereals affected by drought exhibit a premature decline in several vegetation indices. As a result, the best predictors for detecting total crop losses were metrics associated with the later stages of crop development. Specifically, the mean Normalized Difference Vegetation Index (NDVI) for the first half of May showed the highest correlation with drought impact classes (R2 = 0.66). This study is the first to detect total crop losses at the plantation level using field data combined with Sentinel-2 imagery. It also offers insights into rapid monitoring methods for crop failure, an event likely to become more frequent as the climate warms.

Drosophila suzukii (Matsumura), or spotted wing drosophila, has become a major pest concern for berry growers in the United States. In this study, we evaluated the economic impacts of D. suzukii on the Maine wild blueberry industry from two perspectives. The first analysis estimated the state-level economic impacts of D. suzukii on the wild blueberry industry in Maine in the absence of control. We found that D. suzukii could result in drastic revenue losses to the industry, which could be over $6.8 million under the worst-case scenario (assuming a 30% yield reduction). In the second analysis, we used Monte Carlo simulation to compare the expected revenues under different management strategies for a typical wild blueberry farm in Maine. The analysis focused on a decision-making week during the harvesting season, which the grower can choose in between three control strategies: no-control, early harvest, or insecticide application. The results suggested that insecticide applications are not economically optimal in most low infestation risk scenarios. Furthermore, although the early harvest strategy is one of the strategies to avoid D. suzukii infestations for wild blueberry production in Maine, the tradeoff is the revenue loss from the unripe crop. Using the simulation results, we summarized optimal harvest timing regarding the fruit maturity level under different D. suzukii infestation risk scenarios, which can minimize the revenue loss from adopting the early harvest management strategy.

Climate plays a major role in the spatiotemporal distribution of most agricultural systems, and the economic losses related to climate and weather extremes have escalated significantly in the last decades. South America is one of the most productive agricultural areas of the globe. In recent years, remote sensing data and geographic information systems have been used to improve geo-environmental hazard assessment. However, food security is still highly dependent on small farmer practices that are frequently the most vulnerable to climate extremes. This work reviews climate and weather extremes’ impacts on crop production for South American countries, focusing on the projected ones considering different climate scenarios and countries. A positive trend in the productivity of maize, mainly related to agricultural improvements, was recently observed in Colombia, Ecuador, and Uruguay by up to 200%, as well as in the case of soybean in Bolivia and Uruguay by about 125%. Despite the generalized adverse impacts of climate extremes, results from agrometeorological models generally indicate an increase in crop production in southern regions of Chile (and highlands) and Brazil mainly related to increased temperature. Positive impacts in response to CO2 fertilization are also foreseen in Peru and Brazil (southeast, south, and Minas Gerais); in particular, in Brazil, increases in productivity can be raised by about 40%. The use of double-cropping systems, although with very good results in recent years, may also be at risk in a few decades, mainly due to forecasted precipitation decrease, delay in rainy season onset, and temperature increase. The development of timely early warning systems is imperative to produce technically accurate alerts and the interpretation of the risk assessment based on the link between producers and consumers. Promoting climate index insurance is crucial to build resilient food production, but its implementation should rely on regional or international support systems. Moreover, the implementation of adaptation and mitigation also requires climate-resilient technologies that involve an interdisciplinary approach.

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts’ total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R 2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.

The outbreak of Russia-Ukraine conflict casted an impact on the global food market, which was believed to be attributed to that Ukraine has suffered significant production losses due to cropland abandonment. Nevertheless, recent outbreaks of farmer protests against Ukraine’s grain exports demonstrated that the production losses might not be as severe as previous estimates. By utilizing the adaptive threshold segmentation method to extract abandoned cropland from the Sentinel-2 high-resolution imagery and calibrating the spatial production allocation model’s gridded crop production data from Ukraine’s statistical data, this study explicitly evaluated Ukraine’s crop-specific production losses and the spatial heterogeneity. The results demonstrated that the estimated area of abandoned cropland in Ukraine ranges from 2.34 to 2.40 million hectares, constituting 7.14% to 7.30% of the total cropland. In Ukrainian-controlled zones, this area spans 1.44 to 1.48 million hectares, whereas in Russian-occupied areas, it varies from 0.90 to 0.92 million hectares. Additionally, the total production losses for wheat, maize, barley, and sunflower amount to 1.92, 1.67, 0.70, and 0.99 million tons, respectively, with corresponding loss ratios of 9.10%, 7.48%, 9.54%, and 8.67%. Furthermore, production losses of wheat, barley, and sunflower emerged in both the eastern and southern states adjacent to the conflict frontlines, while maize losses were concentrated in the western states. The findings imply that Ukraine ought to streamline the food transportation channels and maintain stable agricultural activities in regions with high crop production.

Floods and droughts are the events that most threaten crop production; however, the impact of floods on crops is still not fully understood and often under-reported. Nowadays, multiple sources of information and approaches support the estimation of agricultural losses due to floods. This study aims to understand the differences in agricultural loss estimates provided by two conceptually different approaches (crop models and expert-based models), evaluating their sensitivity to flood hazard inputs. We investigated the challenges in flood agricultural loss assessments referring to a case study for which, in addition to model simulations, information from surveys and on-site inspections were available. Two crop models (APSIM and WOFOST) and the expert-based model AGRIDE-c were applied to evaluate agricultural yield losses after the flood event of the Panaro River (Emilia-Romagna, Northern Italy) that took place on the 6 December 2020. Two modelling tools were used to reproduce the event: the hydraulic model HEC-RAS and the image-based tool FwDET. Additionally, surveys among local farmers were conducted in the aftermath of the event to evaluate the flood features (water depth, extent and duration) and crop losses. The main findings of the study are that APSIM and WOFOST provide similar estimates of yield losses, while AGRIDE-c tends to underestimate yield losses when the losses over the entire study area are evaluated. The choice of the flood simulation technique does not influence the loss estimation since the difference between the yield loss estimates retrieved from the same model initialized with HEC-RAS or FwDET was always lower than 2%. Information retrieved from the surveys was not sufficient to validate the damage estimates provided by the models but could be used to derive a qualitative picture of the event. Therefore, further research is needed to understand how to effectively incorporate this kind of information in agricultural loss estimation.

Precision agriculture and Unmanned Aerial Vehicles (UAV) are revolutionizing agriculture management methods. Remote sensing data, image analysis and Digital Surface Models derived from Structure from Motion and Multi-View Stereopsis offer new and fast methods to detect the needs of crops, greatly improving crops efficiency. In this study, we present a tool to detect and estimate crop damage after a disturbance (i.e., weather event, wildlife attacks or fires). The types of damage that are addressed in this study affect crop structure (i.e., plants are bent or gone), in the shape of depressions in the crop canopy. The aim of this study was to evaluate the performance of four unsupervised methods based on terrain analyses, for the detection of damaged crops in UAV 3D models: slope detection, variance analysis, geomorphology classification and cloth simulation filter. A full workflow was designed and described in this article that involves the postprocessing of the raw results from the terrain analyses, for a refinement in the detection of damages. Our results show that all four methods performed similarly well after postprocessing––reaching an accuracy above to 90%––in the detection of severe crop damage, without the need of training data. The results of this study suggest that the used methods are effective and independent of the crop type, crop damage and growth stage. However, only severe damages were detected with this workflow. Other factors such as data volume, processing time, number of processing steps and spatial distribution of targets and errors are discussed in this article for the selection of the most appropriate method. Among the four tested methods, slope analysis involves less processing steps, generates the smallest data volume, is the fastest of methods and resulted in best spatial distribution of matches. Thus, it was selected as the most efficient method for crop damage detection.

This research was aimed at understanding how far and how fast glyphosate-resistant (GR) Palmer amaranth will spread in cotton and the consequences associated with allowing a single plant to escape control. Specifically, research was conducted to determine the collective impact of seed dispersal agents on the in-field expansion of GR Palmer amaranth, and any resulting yield reductions in an enhanced GR cotton system where glyphosate was solely used for weed control. Introduction of 20,000 GR Palmer amaranth seed into a 1-m2 circle in February 2008 was used to represent survival through maturity of a single GR female Palmer amaranth escape from the 2007 growing season. The experiment was conducted in four different cotton fields (0.53 to 0.77 ha in size) with no history of Palmer amaranth infestation. In the subsequent year, Palmer amaranth was located as far as 114 m downslope, creating a separate patch. It is believed that rainwater dispersed the seeds from the original area of introduction. In less than 2 yr after introduction, GR Palmer amaranth expanded to the boundaries of all fields, infesting over 20% of the total field area. Spatial regression estimates indicated that no yield penalty was associated with Palmer amaranth density the first year after introduction, which is not surprising since only 0.56% of the field area was infested with GR Palmer amaranth in 2008. Lint yield reductions as high as 17 kg ha−1 were observed 2 yr after the introduction (in 2009). Three years after the introduction (2010), Palmer amaranth infested 95 to 100% of the area in all fields, resulting in complete crop loss since it was impossible to harvest the crop. These results indicate that resistance management options such as a “zero-tolerance threshold” should be used in managing or mitigating the spread of GR Palmer amaranth. This research demonstrates the need for proactive resistance management. Nomenclature: Glyphosate; Palmer amaranth, Amaranthus palmeri S. Wats.; cotton, Gossypium hirsutum L. ‘Stoneville 4554 B2/RRF’.