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Crop lodging is recognised as one of the yield‐limiting factors in agricultural production. Therefore, better understanding to improve lodging resistance and to prevent lodging‐induced losses in agronomic crops is necessary. Besides yield losses, lodging severely affects the crop harvesting process and increases the production cost. However, achieving the objective of higher crop yields and yield quality without increasing lodging risk is quite challenging. To this end, it is essential to interpret the underlying mechanism of plant stem buckling and failure of root anchorage and optimise the fundamental trade‐off between lodging resistance and yield performance in agronomic crops. In the present review, we made an effort to discuss recent and innovative research insights that guarantee greater lodging resistance along with advanced lodging prevention strategies while sustaining higher crop yield and yield quality. [ABSTRACT FROM AUTHOR]

Climate change and variability have threatened rainfed agriculture by affecting the livelihoods of rural communities in Ethiopia. The study area, Gassera District, is among the high-potential crop production areas of the Bale Zone and is severely impacted by recurrent droughts resulting from climate change. This study evaluated smallholder farmers' perceptions of significant climate change and its effects on food crop production across the agroecological zones of the Gassera District. A cross-sectional survey design was employed to collect data from 444 farm households via multistage random sampling techniques. Multiple linear regression (MLR) models were used for the data analysis. The results revealed that 98.5% of the interviewed farmers were aware of climate variability and that 51.6% understood its impact to a reasonable extent. However, over half of the farmers did not perceive climate change as the greatest threat to their livelihood. Most farmers experienced rain becoming more erratic, starting late, and ending early as medium climatic factors (60.4%, 68.1%, and 66.2%, respectively), affecting their crop production. The results revealed that rainfall had a negative and insignificant decreasing trend (2.92 mm/year). The annual mean temperature exhibited a positive and statistically significant increasing trend (Ρ < 0.01). Crop production is positively and linearly correlated with the amount of annual rainfall at the Ρ ≤ 0.05 level of significance. The findings revealed that the greatest crop yield loss was associated with lowland agroecology. The MLR results revealed that farmer agroecology, age, sex, and chemical use had substantial impacts on crop yield loss. We urge farmers to understand the long-term effects of climate change on their livelihoods. [ABSTRACT FROM AUTHOR]

Subsurface (or tile) drainage offers a valuable solution for enhancing crop productivity in poorly drained soils. However, this practice is also associated with significant nutrient leaching, which can contribute to water quality problems at the regional scale. This research presents the findings from a 4-year tile depth and spacing study in central Illinois that included three drain spacings (12.2, 18.3, and 24.4 m) and two drain depths (0.8 and 1.1 m) implemented in six plots under the corn and soybean rotation system (plots CS-1 and CS-3: 12.2 m spacing and 1.1 m depth, plots CS-2 and CS-4: 24.4 m spacing and 1.1 m depth, and plots CS-5 and CS-6 18.3 m spacing and 0.8 m depth). Our observations indicate that drain flow and NO3-N losses were higher in plots with narrower drain spacings, while plots with wider drain spacing showed reduced drain flow and NO3-N losses. Specifically, plots set up with drain spacings of 18.3 m and 24.4 m showed significant reductions in drain flow compared to plots featuring a 12.2 m drain spacing. Likewise, plots characterized by 18.3 m and 24.4 m drain spacings (except CS-4) showed better NO3-N retention and lower leaching losses than those with 12.2 m spacing (CS-1 and CS-3). Crop yield results over a 3-year period indicated that CS-2 (wider spacing plot) showed the highest productivity, with up to 13.6% higher yield compared to other plots. Furthermore, when comparing plots with the same drainage designs, CS-2 and CS-4 showed 5.1% to 2.6% higher corn yield (3-year average) compared to CS-1 and CS-3, and CS-5 and CS-6, respectively. Overall, a wider drainage system showed the capacity to export lower nutrient levels while concurrently enhancing productivity. These findings represent that optimizing tile drainage systems can effectively reduce nitrate losses while increasing crop productivity. [ABSTRACT FROM AUTHOR]

Crop yield losses have escalated worldwide due to extreme and/or prolonged drought periods as well as insufficient irrigation. In the cold and arid regions of northern China, the difficulty of straw decomposition is a regional problem, which is undoubtedly aggravated by the increasingly serious drought. Thus, the combination of a reasonable grain feed rotation mode and tillage mode of straw returning is an effective measure to solve this problem. This study set up a grain forage rotation (in the first year, it is used for grain production, and the straw is returned to the field after the grains are harvested; when the entire plant is harvested in the second year, it is used as feed) and two treatments for straw returning and no straw returning to the land characterized by two different water degresses (include drought stress and normal irrigation). To clarify the impact of returning straw to the field on the soil improvement and drought tolerance of various maize varieties, the effects of straw returning on the agronomic traits, hormones, osmotic adjustment substance content, whole plant fresh weight, and dry weight of six different maize varieties (MC703, A2636, DK 159, ZQ 1, 303 × 178, and ZJ 330) under drought stress were analyzed. The data showed the following: (1) After straw returning, the soil moisture content increased by 3.55–14.70% and the soil bulk density decreased by 0.73–5.23% under the drought stress treatment, and the soil physical condition became closer to the ideal value. (2) After straw returning, the plant height, ear height, stem diameter, fresh weight, and dry weight of the maize whole plant under the drought stress treatment increased by 0.95–7.70%, 4.70–15.71%, 0.31–9.33%, 2.67–36.23%, and 3.72–38.60%, respectively. (3) After straw returning, the abscisic acid, ethylene, soluble sugar, and soluble protein in the leaves under the drought stress treatment decreased by 7.92–21.42%, 8.03–3.37%, 0.63–22.98%, and 2.37–9.17%, respectively. Straw returning significantly alleviated the damage caused by drought stress to the growth and development of maize, and promoted the growth and development of maize under the drought treatment. The results of this study provide a theoretical basis for a high and stable yield of grain crops in arid areas. [ABSTRACT FROM AUTHOR]

In an effort to find alternatives to paraquat for weed control and basal foliage removal in hops (Hummus lupulus L.), due to regulatory and safety concerns, a study was conducted across Oregon and Washington in 2020 and 2021. The study compared the efficacy of tiafenacil and tolpyralate against a nontreated control and carfentrazone. Applications were made early when hops were 2‐ to 3‐m tall (early), and at over 4 m (late). Tiafenacil showed 55%–85% effectiveness in basal foliage control, slightly higher than carfentrazone, and did not cause crop injury. Tiafenacil at 50 and 100 g a.i. ha−1 controlled Lolium multiflorum (Lam.) Husnot (63%). The mixture of tiafenacil and tolpyralate controlled 80% of L. multiflorum, Cirsium arvense L., and Bassia Scoparia (L.) A.J. Scott. In all cases, control was followed by weed regrowth. No signs of crop injury were observed in any of the studies with tiafenacil. Early applications of tiafenacil reduced yield between 0% and 40% relative to the nontreated control. Late applications of tolpyralate and tiafenacil did not significantly reduce yield. Tolpyralate was as effective as carfentrazone for weed control and basal foliage removal. Early applications of tolpyralate reduced plant height and yield relative to the nontreated control and consistently induced phytochemical injury. Tolpyralate yield reductions ranged from 0% to 84% relative to the nontreated control, depending on the trial. A mixture of tolpyralate and tiafenacil was the most effective treatment tested. We conclude that both tiafenacil and tolpyralate are safe for sucker applications in hops, but careful timing is needed to reduce the risk of injury with tolpyralate. The mixture of tiafenacil and tolpyralate can improve weed control comparable to available options. Core Ideas: Tiafenacil and tolpyralate effectively control basal foliage and important weeds in hops.Early applications of tolpyralate and tiafenacil can cause yield loss or crop injury.Tiafenacil and tolpyralate herbicides could replace paraquat for basal foliage removal in hops. [ABSTRACT FROM AUTHOR]

BACKGROUND: Climate change significantly impacts global maize production via yield reduction, posing a threat to global food security. Disease‐related crop damage reduces quality and yield and results in economic losses. However, the occurrence of diseases caused by climate change, and thus crop yield loss, has not been given much attention. RESULTS: This study aims to investigate the potential impact of six major diseases on maize yield loss over the next 20 to 80 years under climate change. To this end, the Maximum Entropy model was implemented, based on Coupled Model Intercomparison Project 6 data. The results indicated that temperature and precipitation are identified as primary limiting factors for disease onset. Southern corn rust was projected to be the most severe disease in the future; with a few of the combined occurrence of all the selected diseases covered in this study were predicted to progressively worsen over time. Yield losses caused by diseases varied per continent, with North America facing the highest loss, followed by Asia, South America, Europe, Africa, and Oceania. CONCLUSION: This study provides a basis for regional projections and global control of maize diseases under future climate conditions. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

ABSTRACT Drought is one of the most devastating causes of yield losses in crops like maize, and the anticipated increases in severity and duration of drought spells due to climate change pose an imminent threat to agricultural productivity. To understand the drought response, phenotypic and molecular studies are typically performed at a given time point after drought onset, representing a steady‐state adaptation response. Because growth is a dynamic process, we monitored the drought response with high temporal resolution and examined cellular and transcriptomic changes after rehydration at 4 and 6 days after leaf four appearance. These data showed that division zone activity is a determinant for full organ growth recovery upon rehydration. Moreover, a prolonged maintenance of cell division by the ectopic expression of PLASTOCHRON1 extends the ability to resume growth after rehydration. The transcriptome analysis indicated that GROWTH‐REGULATING FACTORS (GRFs) affect leaf growth by impacting cell division duration, which was confirmed by a prolonged recovery potential of the GRF1‐overexpression line after rehydration. Finally, we used a multiplex genome editing approach to evaluate the most promising differentially expressed genes from the transcriptome study and as such narrowed down the gene space from 40 to seven genes for future functional characterization. [ABSTRACT FROM AUTHOR]

Background: As identified by the research, it is imperative to develop effective ways to address the pressing problem of disease and pest susceptibility in chili agriculture and secure sustainable crop yield. The research examines the impact of various growing microclimates, watering regimens, and chili cultivars on disease incidence, pest attacks, and yield loss. Results: The study, which took place over a season, used a randomized complete block design to evaluate how well Tanjung, Unpad, and Osaka cultivars performed in four different watering regimens (100, 75, 50, and 25% ETc) and different microclimates (greenhouse, rain shelter, screen house, and open field). The findings exhibited that watering regimens and microclimates greatly influenced disease and pest occurrence, but cultivars had a minimal effect on these variables. Disease and pest attack rates were highest in the open field and lowest in the screen house. A correlation was found between lower disease and pest incidence and optimal irrigation levels (75% and 100% ETc). At lower watering regimens of 25% ETc and in the open field, yield loss was the greatest. Conclusion: The results emphasize how crucial controlled environments and appropriate irrigation techniques are to reducing crop loss and increasing production. Enhancing watering regimens and implementing screen house cultivation are two strategies for improving the productivity and sustainability of chili output. [ABSTRACT FROM AUTHOR]

Waterlogging is a critical abiotic stress increasing in importance due to more intense, erratic rainfall associated with climate change. Waterlogging leads to significant yield losses in sensitive crops, such as soybean (Glycine max [L.] Merr.). Identifying soybean genotypes and traits associated with better waterlogging tolerance is of high interest. We assessed the response of six soybean genotypes, selected from a field screening of over 190 genotypes, to 10 days of waterlogging at the R1 (onset of flowering) and R4 (grain filling) stages. We evaluated yield and its components, as well as shoot and root dry weights (DW) at the end of the waterlogging treatments and at maturity, along with morphological traits such as plant branch number, stem diameter and plant height. By integrating all these traits, a waterlogging tolerance index (WTI) was calculated for each genotype to rank their sensitivity. The WTI showed variations among genotypes from 0.61 to 0.77, indicating genotypic variation in response to waterlogging. Greater reductions in root DW compared to shoot DW were observed immediately after waterlogging. By maturity, shoot DW of waterlogged plants was more severely reduced than root DW in all genotypes. Despite similar DW losses at R1 and R4 at physiological maturity, seed number per plant and 100‐seed weight responses differed between the treatments. Genotypes that performed well under control conditions suffered significant yield reductions of 70%–85% after waterlogging, mainly due to fewer fertile nodes and seeds per pod, with some also experiencing a notable decrease in 100‐seed weight. In contrast, other genotypes had milder responses, with less severe reductions in seed and pod traits. Identifying breeding soybean genotypes tolerant to waterlogging during reproductive stages that maintain the number of fertile nodes and pods per node without changes in seeds per pod could significantly mitigate yield losses from waterlogging. [ABSTRACT FROM AUTHOR]

This study quantitatively evaluates the effectiveness of three main options for the adaptation of crop farming to climate change (i.e., shift of planting dates, increase/addition of irrigation, and resilient hybrids/cultivars) in Greece, a country in southern Europe. The potential effect of each option on the yields of several crops in all Greek regions is estimated for 2021–2040 and 2041–2060 and compared with those under the historical local climate of 1986–2005, by using agronomic and statistical regression models, and data from different climatic simulations and climate change scenarios. Our results reveal that all the adaptation options examined have the potential to significantly reduce crop yield losses occurring under no adaptation, particularly during 2021–2040 when for many regions and crops more than half of the losses can be compensated for. Notably, in some cases during this period, the measures examined resulted in crop yields that are higher than those under the historical climate. However, the effectiveness of the measures diminished significantly in 2041–2060 under very adverse climate change conditions, highlighting the dynamic nature of adaptation. Assessing the effectiveness of combined adaptation options and evaluating additional criteria (e.g., feasibility) represent essential areas for future research. [ABSTRACT FROM AUTHOR]

Recently, the decline in plant species, loss of crop yields, and reduced efficacy of herbal medicines due to environmental issues and biotic stresses have garnered significant attention. Developing smart devices for plant health monitoring is essential for early intervention, timely adjustment of the growing environment to combat environmental and pest stresses, and to promote robust plant growth, biodiversity, ecological balance, and sustainable agriculture. Flexible wearable sensors with the advantages of superior shape adaptation, excellent biocompatibility, and high integration have emerged as one of the most promising avenues for plant health monitoring. Here, recent advances in flexible wearable sensors based on monitoring different types of plant signals are summarized. The discussion focuses on the constituent materials, fabrication methods, and sensing mechanisms of each type of wearable sensor. In addition, the challenges and potential strategies are summarized for plant sensing development, including energy supply, materials and preparation, signal transmission, and data analysis. [ABSTRACT FROM AUTHOR]

This study proposed a framework using artificial neural networks (ANNs), the designed residual regression method, and probabilistic indexing, for drought sensitivity shift analysis, associated with future yield loss risk in Anthropocene. The temporal assessment of the drought index (standardized precipitation evapotranspiration index, SPEI) illustrated prevalent occurrence of mild to moderate drought with a fluctuating trend. Furthermore, the optimal ANN model, with R2 = 0.86 for training and 0.7 for testing, was used to project yield for future analysis. The projected spatial changes in drought sensitivity patterns suggest most of the area will experience an increased sensitivity to drought in the far future (2036–2050). The estimated probability loss under the different risk levels defined by the yield reduction rate was 6.2–55.5%. Furthermore, the spatial distribution of the designed risk index (IR) revealed the districts with the highest probability of vulnerability and showed a probable increase in future vulnerability. [ABSTRACT FROM AUTHOR]

The productivity of the soybean crop (Glycine max) can be limited by several factors, including diseases. In the integrated management of diseases, one of the strategies used is chemical control. The aim of this experiment was to evaluate the influence of spray nozzle types on the chemical control of fungal diseases in soybean plants. The experimental design was randomised blocks, with six treatments and four replications. The treatments consisted of control (without fungicide application on soybean plants) and application of fungicides with different spray nozzles: hollow cone jet, double flat jet, extended range flat jet, wide angle flat jet and air induction flat jet. The sprayer used was the backpack pressurised by carbon dioxide CO2. The experiment was installed in the Campos Gerais region (Paraná, Brazil); during three cropping seasons. The evaluated variables were incidence and severity, spray quality (covered area, droplet density, and relative amplitude) and yield components. It was concluded that the chemical control of fungal diseases in soybean plants reduced the proliferation of pathogens and losses in crop yield. There were significant changes in spraying quality in all analysed variables. There were no differences among the spray nozzle types regarding the analysis of incidence, severity and soybean yield components. [ABSTRACT FROM AUTHOR]

Soil salinity is a major contributor to crop yield losses. To improve our understanding of root responses to salinity, we developed and exploited a real-time salt-induced tilting assay. This assay follows root growth upon both gravitropic and salt challenges, revealing that root bending upon tilting is modulated by Na+ ions, but not by osmotic stress. Next, we measured this salt-specific response in 345 natural Arabidopsis (Arabidopsis thaliana) accessions and discovered a genetic locus, encoding the cell wall-modifying enzyme EXTENSIN ARABINOSE DEFICIENT TRANSFERASE (ExAD) that is associated with root bending in the presence of NaCl (hereafter salt). Extensins are a class of structural cell wall glycoproteins known as hydroxyproline (Hyp)-rich glycoproteins, which are posttranslationally modified by O-glycosylation, mostly involving Hyp-arabinosylation. We show that salt-induced ExAD-dependent Hyp-arabinosylation influences root bending responses and cell wall thickness. Roots of exad1 mutant seedlings, which lack Hyp-arabinosylation of extensin, displayed increased thickness of root epidermal cell walls and greater cell wall porosity. They also showed altered gravitropic root bending in salt conditions and a reduced salt-avoidance response. Our results suggest that extensin modification via Hyp-arabinosylation is a unique salt-specific cellular process required for the directional response of roots exposed to salinity. Salt induces EXTENSIN ARABINOSE DEFICIENT TRANSFERASE-dependent arabinosylation of hydroxyproline residues in extensin to modify cell wall structure and the gravitropism response of roots. [ABSTRACT FROM AUTHOR]

Early blight of tomato caused by Alternaria solani is one of the most important and devastating disease of tomato which causing extensive yield losses to the crop. Environmental factors play an important role in the development of disease. To investigate the congenial conditions to develop the disease and germination of fungal spores an experiment was conducted in the year 2019 and 2020 at vegetable pathology laboratory of Dr Yashwant Singh Parmar University of Horticulture and Forestry Nauni Solan, Himachal Pradesh, India. Under investigations effect of temperature and relative humidity (RH) on conidial germination and early blight diseases development of tomato caused by Alternaria solani was studied under in vitro and in vivo conditions. Under in vitro conditions, maximum germination of conidia (75.54%) was recorded at 30°C after 24 hours and minimum (23.24%) was recorded at 15°C. length of germ tube (129.55 µm) was maximum at 25°C, whereas, minimum (67.09 µm) was observed at 15°C. In case of relative humidity maximum conidial germination (95.18%) and germ tube length of 238.95 µm was observed at 100% level. Under in vivo conditions disease severity was maximum (81.48%) at 30°C followed by 25°C (78.21%), whereas, minimum (18.88%) was recorded at 40°C. Similarly, the maximum (88.09%) disease severity was observed at 100% RH level while, it was minimum at 38% level of humidity. Overall, the relative humidity of more than 90% and temperature range of 25-30°C was found most congenial for the development of disease. [ABSTRACT FROM AUTHOR]

Fusarium graminearum, a devastating fungal pathogen, causes great economic losses to crop yields worldwide. The present study investigated the potential of Streptomyces pratensis S10 to alleviate F. graminearum stress in wheat seedlings based on plant growth-promoting and resistance-inducing assays. The bioassays revealed that S10 exhibited multiple plant growth-promoting properties, including the production of siderophores, 1-aminocyclopropane-1-carboxylic acid deaminase (ACC), and indole-3-acetic acid (IAA), phosphate solubilization, and nitrogen fixation. Meanwhile, the pot experiment demonstrated that S10 improved wheat plant development, substantially enhancing wheat height, weight, root activity, and chlorophyll content. Consistently, genome mining identified abundant genes associated with plant growth promotion. S10 induced resistance against F. graminearum in wheat seedlings. The disease incidence and disease index reduced by nearly 52% and 65% in S10 pretreated wheat seedlings, respectively, compared with those infected with F. graminearum only in the non-contact inoculation assay. Moreover, S10 enhanced callose deposition and reactive oxygen species (ROS) accumulation and induced the activities of CAT, SOD, POD, PAL, and PPO. Furthermore, the quantitative real-time PCR (qRT-PCR) results indicated that S10 pretreatment increased the expression of SA- (PR1.1, PR2, PR5, and PAL1) and JA/ET-related genes (PR3, PR4a, PR9, and PDF1.2) in wheat seedlings upon F. graminearum infection. In summary, S. pratensis S10 could be an integrated biological agent and biofertilizer in wheat seedling blight management and plant productivity enhancement. [ABSTRACT FROM AUTHOR]

This study objects are juices from melon, watermelon, and pumpkin, as well as additives from apples, plums, pears, bell peppers, beets, carrots, aloe, melissa, and nettle. The problem addressed is minimizing melon crop losses. The research relevance stems from the need to reduce these losses. The development of multicomponent juices is aimed at the more efficient utilization of melon crops, contributing to the minimization of crop losses. Optimal component proportions were determined using sensory evaluation of organoleptic characteristics on a 5-point scale. The results showed that juice compositions with optimal component proportions have high organoleptic qualities and functional orientation. For melon juice, the optimal ratio was as follows: melon juice – 55 %, apple juice – 30 %, green bell pepper juice – 10 %, aloe juice – 5 %. For watermelon juice: watermelon juice – 65 %, plum juice – 25 %, beet juice – 5 %, melissa juice – 5 %. For pumpkin juice: pumpkin juice – 60 %, pear juice – 20 %, carrot juice – 15 %, nettle juice – 5 %. Critical control point (CCP) analysis was conducted at each production stage, from raw material acceptance to packaging and storage of the final product. This analysis identified potential risks and established control measures to minimize them. Developing multicomponent juices based on melon crops with plant based enriching additives allows the creation of products with high organoleptic qualities and functional orientation, contributing to efficient use of melon crops and reducing crop losses. Implementing the HACCP system at all production stages ensures product safety and high quality. This research offers a practical approach for the food industry to produce high-quality, functional beverages that maximize crop yields and ensure consumer safety. [ABSTRACT FROM AUTHOR]

The application of biogas slurry in agriculture production is regarded as a sustainable method for mitigating the environmental impacts of fertilization. To investigate the effects of biogas slurry application on soil nitrogen loss and crop yield, a field plot experiment was conducted within a wheat–maize rotation system. This study assessed the effects of three levels of biogas slurry nitrogen substitution, 50% (BSF), 100% (BS), and 150% (EBS), on the yield of silage maize and wheat, nitrogen use efficiency, and soil nitrogen loss. The findings revealed that in the first year (characterized by high rainfall), the application of the biogas slurry led to increased NH3 emissions and nitrogen leaching, resulting in a notable rise in the annual nitrogen loss. Additionally, it was observed that as the amount of applied biogas slurry increased, the nitrogen loss also rose correspondingly. However, in the second year (a period of drought conditions), despite the elevated NH3 emissions from the biogas slurry, there was a significant reduction in nitrogen leaching, which resulted in reductions of 14.2% and 20.0% in annual nitrogen loss for the BSF and BS treatments, respectively, with comparable nitrogen input to the fertilizer treatment. Throughout both years, the application of biogas slurry did not decrease the yield of silage maize and wheat, and notably, the BS treatment even enhanced the crop nitrogen utilization efficiency. Compared with other nitrogen fertilizer treatments, the EBS treatment did not increase crop yield even with an increased nitrogen application rate; it also reduced the nitrogen utilization efficiency and N loss. In conclusion, employing biogas slurry to replace chemical fertilizer (equivalent nitrogen substitution) during drought years can enhance nitrogen utilization efficiency, reduce nitrogen loss, and sustain crop yield. When applying biogas slurry in years with substantial rainfall, effective measures should be implemented to mitigate nitrogen loss. [ABSTRACT FROM AUTHOR]

Salinity stress has a great impact on crop growth and productivity and is one of the major factors responsible for crop yield losses. The K-homologous (KH) family proteins play vital roles in regulating plant development and responding to abiotic stress in plants. However, the systematic characterization of the KH family in rice is still lacking. In this study, we performed genome-wide identification and functional analysis of KH family genes and identified a total of 31 KH genes in rice. According to the homologs of KH genes in Arabidopsis thaliana, we constructed a phylogenetic tree with 61 KH genes containing 31 KH genes in Oryza sativa and 30 KH genes in Arabidopsis thaliana and separated them into three major groups. In silico tissue expression analysis showed that the OsKH genes are constitutively expressed. The qRT-PCR results revealed that eight OsKH genes responded strongly to salt stresses, and OsKH12 exhibited the strongest decrease in expression level, which was selected for further study. We generated the Oskh12-knockout mutant via the CRISPR/Cas9 genome-editing method. Further stress treatment and biochemical assays confirmed that Oskh12 mutant was more salt-sensitive than Nip and the expression of several key salt-tolerant genes in Oskh12 was significantly reduced. Taken together, our results shed light on the understanding of the KH family and provide a theoretical basis for future abiotic stress studies in rice. [ABSTRACT FROM AUTHOR]

Changes in the hydrologic regime impose great challenges for grain production. We investigated the impact of dry and wet extremes on the recent losses of crops in Severo-Kazakhstanskaya Oblast (SKO), where 25% of Kazakhstan's wheat is produced. We reconstructed the Palmer Drought Severity Index (June–August PDSI) and average grain yields (with an explained variance of 48% and 44%, respectively) using five tree ring width chronologies. The extended history of the moisture variability and yields of spring wheat, oats, and barley shows the strong impact of hydrology, rather than the heat, on the grain production. We defined three distinctive hydrologic regimes in SKO: (1) 1886–1942, (2) 1943–1977, (3) 1978–2023. The early regime had fewer drought events, including some that covered a single year. Their duration increased up to 3 years in the second period. The latest regime is an extreme mode of hydrologic variability with events abruptly switching from extremely dry to extremely wet conditions (called "whiplash"). The 21st century regime signifies that the intensified and prolonged decade-long drought transitioned into pluvial condition. The new regime created sizable instability for grain producers. This crop yield reconstruction denotes the potential of the tree-ring proxy for understanding the impact of climate change on the agriculture and food security of Central Asia. [ABSTRACT FROM AUTHOR]

Plant diseases, particularly leaf diseases, are one of the factors contributing to crop loss, accounting for approximately 15-40% of the total loss in crop production. Early identification of these diseases is crucial in maximizing crop yield. However, existing models struggle to accurately identify leaf diseases as they manifest in images as irregularly shaped spots, and a single leaf can exhibit multiple disease symptoms, making it a multi-label classification problem. To address this issue, a novel variant of the Region Vision Transformer architecture called Class attention enabled RegionViT (CaRiT) has been proposed for plant leaf disease classification. CaRiT incorporates a class attention mechanism that allows the model to capture the spatial relationships between patches, enabling accurate multi-label disease classification. CaRiT model performance was evaluated on imbalanced datasets, namely Dataset-I1 and Dataset-I2, as well as a balanced dataset called Dataset-B. The CaRiT model achieved the average ROC-AUC values of 0.946 for Dataset-I1, 0.936 for Dataset-I2, and 0.953 for Dataset-B. The CaRiT model is compared with DenseNet, ResNeSt, ResNeXt, and RegionViT. These models achieved average ROC-AUC values ranging from 0.810 to 0.931, 0.811 to 0.930, and 0.650 to 0.940 for Dataset-I1, Dataset-I2, and Dataset-B respectively. These results demonstrate the consistent outperformance of the CaRiT model when compared to the other models on all three datasets. [ABSTRACT FROM AUTHOR]

• Integration of Sentinel 1 and Sentinel 2 for time-series analysis of soybean yield prediction. • Utilized machine learning techniques to monitor crop growth during peak phenological periods. • Incorporated environmental data, Topographic Wetness Index (TWI), for improved yield predictions. • Applied correlation-based feature selection to identify key features among the predictors. It is crucial to accurately and timely estimate crop yield within field variability for sustainable management and precision farming applications. Various Earth observation systems have been developed for crop monitoring and yield prediction. However, there is a need for further research that integrates multiplatform data, advances in satellite technology, and data processing to apply this knowledge to agricultural practices. The integration of satellite imagery and environmental data has been used increasingly in recent years to predict crop yields using machine learning techniques. In recent years, VIs derived from optical satellites, particularly Sentinel 2 (S2), have gained popularity, but their availability is affected by weather conditions. On the other hand, the backscatter data from Sentinel 1 (S1) is less commonly used in agriculture due to its complex interpretation and processing, but it is not influenced by the weather. This study aims to improve the accuracy of yield predictions by combining remote sensing data with environmental variables. The use of satellite data S1 and S2 was used to identify the optimal phenological period, and a training model was developed using four machine learning techniques, including Random Forest Regression (RF), K Nearest Neighbor (KNN), Multiple Linear Regression (MLR) and Decision Tree (DT). The results showed that RF provided the highest values among the four techniques. The validation process using RF demonstrated high accuracy rates, with R2 ranging from 0.41 to 0.89, the mean square error of the root (RMSE) ranging from 0.122 to 0.224 t/ha, and the mean absolute error (MAE) ranging from 0.089 to 0.163 t/ha. The integration of satellite data S1 and S2 with topographical information may be useful for monitoring, mapping, and forecasting crop yields on small and fragmented farmlands. This approach can provide farmers, agricultural businesses, and policymakers with accurate and timely predictions of crop yield, which can facilitate decision making and provide early warnings for potential crop losses. [ABSTRACT FROM AUTHOR]

The study explores the feasibility of adapting the EOStat crop monitoring system, originally designed for monitoring crop growth conditions in Poland, to fulfill the requirements of a similar system in Ukraine. The system utilizes satellite data and agrometeorological information provided by the Copernicus program, which offers these resources free of charge. To predict crop yields, the system uses several factors, such as vegetation condition indices obtained from Sentinel-3 Ocean and Land Color Instrument (OLCI) optical and Sea and Land Surface Temperature Radiometer (SLSTR). It also incorporates climate information, including air temperature, total precipitation, surface radiation, and soil moisture. To identify the best predictors for each administrative unit, the study utilizes a recursive feature elimination method and employs the Extreme Gradient Boosting regressor, a machine learning algorithm, to forecast crop yields. The analysis indicates a noticeable decrease in crop losses in 2022 in certain regions of Ukraine, compared to the previous year (2021) and the 5-year average (2017–2021), specifically for winter crops and maize. Considering the reduction in yield, it is estimated that the decline in production of winter crops in 2022 was up to 20%, while for maize, it was up to 50% compared to the decline in production. [ABSTRACT FROM AUTHOR]

Globally, pest-induced crop losses ranging from 20% to 40% have spurred the extensive use of pesticides, presenting a double-edged sword that threatens not only human health but also our environment. Amidst various remediation techniques, bioremediation stands out as a compelling and eco-friendly solution. Recently, the phytomicrobiome has garnered increasing attention as endophytic microbes, colonizing plants from their roots, not only foster plant growth but also enhance the host plant's resilience to adverse conditions. Given the persistent demand for high crop yields, agricultural soils often bear the burden of pesticide applications. Biodegradation, the transformation of complex pesticide compounds into simpler forms through the activation of microbial processes and plant-based enzymatic systems, emerges as a pivotal strategy for restoring soil health. Manipulating the phytomicrobiome may emerge as a viable solution for this purpose, offering a native metabolic pathway that catalyzes pollutant degradation through enzymatic reactions. This review delves into the pivotal role of phytomicrobiomes in the degradation of diverse pesticides in soil. It explores contemporary innovations and paves the way for discussions on future research directions in this promising field. [ABSTRACT FROM AUTHOR]

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss and minimize them. Diseases are one of the major causes that adversely affect crop yield and degrade the quality of the tomato fruit. This leads to financial losses and affects the livelihood of farmers. Therefore, automatic disease detection at any stage of the tomato plant is a critical issue. Deep learning models introduced in the literature show promising results, but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of parameters. Also, most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf region. Moreover, the existing techniques lack in recognizing multiple diseases on the same leaf. To address these concerns, we introduce a customized deep learning-based convolution vision transformermodel. The model achieves an accuracy of 93.51% for classifying tomato leaf images with plain as well as complex backgrounds into 13 categories. It requires a space storage of merely 5.8 MB which is 98.93%, 98.33%, and 92.64% less than state-of-the-art visual geometry group, vision transformers, and convolution vision transformermodels, respectively. Its training time of 44 min is 51.12%, 74.12%, and 57.7% lower than the above-mentioned models. Thus, it can be deployed on (Internet of Things) IoT-enabled devices, drones, or mobile devices to assist farmers in the real-time monitoring of tomato crops. The periodicmonitoring promotes timely action to prevent the spread of diseases and reduce crop loss. [ABSTRACT FROM AUTHOR]

Currently, everyone is facing significant difficulties with food scarcity. There may be several causes for this, but food loss is a well‐known issue. Specifically, crop losses bring on by plant and leaf diseases during farming operations. Plant disease is typically identified visually or through laboratory testing, which delays detection and reduces crop yields by the time it is finished. The wide use of smartphones along with recent advancements in computer vision has made it viable to diagnose any ailment by applying machine learning techniques. Smartphone‐assisted disease diagnosis is now a reality. In this study, a multi‐Support Vector Machine (SVM) model is used to detect four different diseases as well as healthy conditions of plant leaves. Using 13 feature vectors for each input, a total of 2400 leaves representing four distinct classes (four varieties) have been used to train and test the model. The experimental results give an average accuracy of 91.25% for diseased leaves detection and 99% accuracy for healthy leaves detection. [ABSTRACT FROM AUTHOR]

Due to the increasing impact of worldwide environmental changes, temperature stress has become a major factor resulting in crop yield losses. The discovery of temperature‐stress‐responsive protein‐coding genes has made significant progress in understanding plants' complex stress response systems involving microRNAs (miRNAs). The miRNAs are triggered by heat or cold, thus confirming their significant functional role in cold or heat tolerance. Such dependable recommendations significantly broaden our understanding of the regulatory role of miRNAs in plant stress responses. This article presents novel perspectives on the substantial roles of plant miRNAs in responding to and acclimatizing to heat and cold stress. It comprehensively elaborates on miRNAs responsive to temperature stress, their regulatory mechanisms, and their targeted functions in plants. Additionally, the article investigates how miRNAs contribute to safeguarding plant reproductive tissues, mitigating damage caused by reactive oxygen species, modulating heat shock proteins, transcription factors, and phytohormones in the context of temperature stress. The conclusion outlines potential avenues for future research, highlighting the utilization of miRNAs and their regulatory functions to develop economically vital crops with enhanced tolerance to temperature stress, thereby ensuring future food security. [ABSTRACT FROM AUTHOR]

Summary: COLD is a major naturally occurring stress that usually causes complex symptoms and severe yield loss in crops. R‐loops function in various cellular processes, including development and stress responses, in plants. However, how R‐loops function in COLD responses is largely unknown in COLD susceptible crops like rice (Oryza sativa L.).We conducted DRIP‐Seq along with other omics data (RNA‐Seq, DNase‐Seq and ChIP‐Seq) in rice with or without COLD treatment.COLD treatment caused R‐loop reprogramming across the genome. COLD‐biased R‐loops had higher GC content and novel motifs for the binding of distinct transcription factors (TFs). Moreover, R‐loops can directly/indirectly modulate the transcription of a subset of COLD‐responsive genes, which can be mediated by R‐loop overlapping TF‐centered or cis‐regulatory element‐related regulatory networks and lncRNAs, accounting for c. 60% of COLD‐induced expression of differential genes in rice, which is different from the findings in Arabidopsis. We validated two R‐loop loci with contrasting (negative/positive) roles in the regulation of two individual COLD‐responsive gene expression, as potential targets for enhanced COLD resistance.Our study provides detailed evidence showing functions of R‐loop reprogramming during COLD responses and provides some potential R‐loop loci for genetic and epigenetic manipulation toward breeding of rice varieties with enhanced COLD tolerance. [ABSTRACT FROM AUTHOR]

Bacterial wilt, caused by Ralstonia solanacearum, one of the most destructive phytopathogens, leads to significant annual crop yield losses. Type III effectors (T3Es) mainly contribute to the virulence of R. solanacearum, usually by targeting immune-related proteins. Here, we clarified the effect of a novel E3 ubiquitin ligase (NEL) T3E, RipAW, from R. solanacearum on pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and further explored its action mechanism. In the susceptible host Arabidopsis thaliana, we monitored the expression of PTI marker genes, flg22-induced ROS burst, and callose deposition in RipAW- and RipAWC177A-transgenic plants. Our results demonstrated that RipAW suppressed host PTI in an NEL-dependent manner. By Split-Luciferase Complementation, Bimolecular Fluorescent Complimentary, and Co-Immunoprecipitation assays, we further showed that RipAW associated with three crucial components of the immune receptor complex, namely FLS2, XLG2, and BIK1. Furthermore, RipAW elevated the ubiquitination levels of FLS2, XLG2, and BIK1, accelerating their degradation via the 26S proteasome pathway. Additionally, co-expression of FLS2, XLG2, or BIK1 with RipAW partially but significantly restored the RipAW-suppressed ROS burst, confirming the involvement of the immune receptor complex in RipAW-regulated PTI. Overall, our results indicate that RipAW impairs host PTI by disrupting the immune receptor complex. Our findings provide new insights into the virulence mechanism of R. solanacearum. [ABSTRACT FROM AUTHOR]

Surface ozone (O 3) is well known for posing significant threats to both human health and crop production worldwide. However, a multidecadal assessment of the impacts of O 3 on public health and crop yields in China is lacking due to insufficient long-term continuous O 3 observations. In this study, we used a machine learning (ML) algorithm to correct the biases of O 3 concentrations simulated by a chemical transport model from 1981–2019 by integrating multi-source datasets. The ML-enabled bias correction offers improved performance in reproducing observed O 3 concentrations and thus further improves our estimates of the impacts of O 3 on human health and crop yields. The warm-season trends of increasing O 3 in Beijing–Tianjin–Hebei and its surroundings (BTHs) as well as in the Yangtze River Delta (YRD), Sichuan Basin (SCB), and Pearl River Delta (PRD) regions are 0.32, 0.63, 0.84, and 0.81 µ g m -3 yr -1 from 1981 to 2019, respectively. In more recent years, O 3 concentrations experienced more fluctuations in the four major regions. Our results show that only BTHs have a perceptible increasing trend of 0.81 µ g m -3 yr -1 during 2013–2019. Using accumulated O 3 over a threshold of 40 ppb (AOT40-China) exposure–yield response relationships, the estimated relative yield losses (RYLs) for wheat, rice, soybean, and maize are 17.6 %, 13.8 %, 11.3 %, and 7.3 % in 1981, increasing to 24.2 %, 17.5 %, 16.3 %, and 9.8 % in 2019, with an increasing rate of +0.03 % yr -1 , +0.04 % yr -1 , +0.27 % yr -1 , and +0.13 % yr -1 , respectively. The estimated annual all-cause premature deaths induced by O 3 increased from ∼55900 in 1981 to ∼162000 in 2019 with an increasing trend of ∼2980 deaths per year. The annual premature deaths related to respiratory and cardiovascular disease are ∼34200 and ∼40300 in 1998 and ∼26500 and ∼79000 in 2019, having a rate of change of -546 and +1770 deaths per year during 1998–2019, respectively. Our study, for the first time, used ML to provide a robust dataset of O 3 concentrations over the past 4 decades in China, enabling a long-term evaluation of O 3 -induced crop losses and health impacts. These findings are expected to fill the gap of the long-term O 3 trend and impact assessment in China. [ABSTRACT FROM AUTHOR]

Extreme dry and wet events can result in significant crop yield losses. However, the impact of consecutive occurrence of dry and wet extremes on crop yield remains unclear. Here, we investigate the hotspots of compound dry and wet (CDW) extremes across global rice croplands and their impacts on rice yield. We identify a significant increasing trend in the frequency of CDW extremes during 1981–2016. The risk of yield loss caused by CDW extremes can be twice as high as the risk from individual wet and dry extremes. Furthermore, we find that global rice croplands face a 43% higher risk of rice yield loss due to dry‐to‐wet extremes compared to wet‐to‐dry extremes. Our findings provide new insights into the sustainability of global rice production and food security in the face of compound hydrological extremes. Plain Language Summary: It is widely recognized that compound events may exert larger impacts on crop production compared to individual extremes. Here, we investigate the consecutive occurrence of dry and wet (CDW) extremes during the rice‐growing season and estimate their impacts on rice yield. We observe a significant increase in the frequency of CDW extremes across global rice croplands during the rice‐growing season from 1981 to 2016. The CDW extremes exert a larger impact on rice yield loss compared to individual wet and dry extremes. The CDW extremes, characterized by longer durations of both dry and wet extremes and faster transitions between them, have an even more adverse influence on rice yield. The risk of yield loss caused by CDW extremes is 200% higher than the risk from individual wet and dry extremes. Furthermore, global rice croplands face a 43% higher risk of yield loss due to dry‐to‐wet extremes than wet‐to‐dry extremes. Key Points: A significant increasing trend in the frequency of compound dry and wet (CDW) extremes was observed across global rice croplandsThe risk of rice yield loss caused by CDW extremes can be twice as high as the risk from individual wet and dry extremesGlobal rice croplands face a 43% higher risk of rice yield loss caused by dry‐to‐wet extremes compared to wet‐to‐dry extremes [ABSTRACT FROM AUTHOR]

Salt stress affects plant growth and development, resulting in the loss of crop yield across the world, and sodium-proton antiporters (NHXs) are one of the genes known to promote salt tolerance in transgenic plants. In this study, we conducted a comprehensive genome-wide analysis and expression profile of NHX genes in wheat under salinity stress. We identified 30 TaNHX genes in wheat based on the Na+/H+ exchanger domain, with all genes containing an amiloride motif except one, a known for inhibiting Na+ ions in plants. Phylogenetic analysis classified these genes into three classes with subfamilies: 12 were localized in vacuoles, while 18 were in the endoplasmic reticulum and plasma membrane. Promoter analysis revealed stress-related cis-acting elements, indicating their potential role in abiotic stress tolerance. The non-synonymous (Ka)/synonymous (Ks) ratios highlighted that the majority of TaNHX genes experienced robust purifying selection throughout their evolutionary history. Transcriptomis data analysis and qRT-PCR demonstrated distinct expression patterns for TaNHX genes across various tissues when subjected to salt stress. Additionally, we predicted 20 different miRNA candidates targeting the identified TaNHX genes. Protein-protein interaction prediction revealed NHX6’s involvement in the SOS1 pathway, while NHX1 gene exhibit proton antiporter activity. Molecular dynamics (MD) simulations were also conducted to examine the interactions of TaNHX1, TaNHX2, and TaNHX3. These results represent a significant advancement in our understanding of the molecular mechanisms governing Na+ transporters. This may also offer promising avenues for future studies aimed at unraveling the intricate details of their biological roles and applications. [ABSTRACT FROM AUTHOR]

This paper studies the effects of drought shocks in a vulnerable environment – the Brazilian Semi-Arid. We analyze the impact of drought shocks, measured as deviations from long-run historical averages, on agricultural outcomes in a region that suffers recurrently from drought. After controlling for municipality and year fixed effects, we use weather shocks to exactly identify outcomes. Our benchmark results show substantial effects on the loss of crop area and on the value of agricultural output, as well as on crop yields. As we investigate distributional effects, our results show that crops related to familiar agriculture suffer more from drought shocks. We follow our investigation by testing heterogeneity effects and show that adequate water provision and maintenance of forest cover help in reducing the impact of drought shocks. [ABSTRACT FROM AUTHOR]

The prevailing environmental conditions control the growth and development of plant in one way or the other throughout the world. Abiotic stresses, i.e., drought, heavy metals, salinity, heat and cold and other environmental extremes negatively influence survival of plants and reduce the productivity of crops. The world's most popular grain, wheat (Triticum aestivum L.), is highly susceptible to abiotic stress, which is the major reason for yield losses in the crop. Silicon (Si) is the second most abundant element and gaining popularity for its multilateral role in providing significant advantages for growth and yield of crop plants. It is a non-essential nutrient, but it has been shown to have beneficial effects on the growth and development of several plant species. Si substantially reduces the most abiotic stresses and it is non-polluting, non-detrimental and non-harmful to plants even when used in large quantities. Exogenous Si application prevents the detrimental effect of drought, heat and cold stress by increasing cuticle and epidermal layer thickening from silica deposited. Si creates an apoplastic barrier in the endodermis closer to the root apex to prevent the uptake and mobility of heavy metals. Under adverse conditions, Si increases nutrient uptake and restores wheat growth and production. The review clearly reveals the importance of Si nutrition in imparting abiotic stress tolerance and the need for its application in the wheat crop. Plant producers can benefit from Si fertilizers both economically and environmentally. [ABSTRACT FROM AUTHOR]

Food demand is directly associated with the human population. Due to various plant diseases, there has been a reduction in crop yield. There is an extreme necessity to low such losses in crop yield to meet the rising demand for food. Novel and eco-friendly control approaches should be developed for combating bacterial diseases of crops. Recent control strategies that involve the usage of antibiotics or chemicals are no more effective because of resistance developed by bacterial species. Furthermore, the usage of such agents has proven to be not environmentally friendly. To overcome these issues, bacteriophages are used as an alternative solution. Phages are viruses that attack specific bacterial species, and within current years much consideration is received by them in controlling different diseases caused by bacteria. Phages can be used for controlling different crop-related diseases. Several phage-based products are accessible in the market. Compared to chemical control methods, phage biocontrol offers several advantages. Mixtures of phages can be employed to target pathogenic bacteria. Unlike chemical control strategies, phage mixtures can be readily adjusted to counter any potential resistance. This review summarizes the use of phages as a biocontrol agent against phytopathogens. [ABSTRACT FROM AUTHOR]

Plant-parasitic nematodes (PPN), especially sedentary endoparasitic nematodes like root-knot nematodes (RKN), pose a significant threat to major crops and vegetables. They are responsible for causing substantial yield losses, leading to economic consequences, and impacting the global food supply. The identification of PPNs and the assessment of their population is a tedious and time-consuming task. This study developed a state-of-the-art deep learning model-based decision support tool to detect and estimate the nematode population. The decision support tool is integrated with the fast inferencing YOLOv5 model and used pretrained nematode weight to detect plant-parasitic nematodes (juveniles) and eggs. The performance of the YOLOv5-640 model at detecting RKN eggs was as follows: precision = 0.992; recall = 0.959; F1-score = 0.975; and mAP = 0.979. YOLOv5-640 was able to detect RKN eggs with an inference time of 3.9 milliseconds, which is faster compared to other detection methods. The deep learning framework was integrated into a user-friendly web application system to build a fast and reliable prototype nematode decision support tool (NemDST). The NemDST facilitates farmers/growers to input image data, assess the nematode population, track the population growths, and recommend immediate actions necessary to control nematode infestation. This tool has the potential for rapid assessment of the nematode population to minimise crop yield losses and enhance financial outcomes. [ABSTRACT FROM AUTHOR]

Bacterial canker of stone fruits caused by Pseudomonas syringae pv. syringae (Pss) and Pseudomonas syringae pv. morsprunorum (Psm race‐1/Psm race‐2) may lead to significant yield and crop losses in apricot (Prunus armeniaca L.) cultivation areas in Türkiye. Strains pathogenic to apricot were isolated from trees with symptoms (mainly necrotized buds and dieback) of bacterial canker in orchards in Aras Basin. Pathogens were characterized using pathogenicity tests, phenotypic assays, end‐point PCR and multilocus sequence analysis (MLSA). Fifteen Pseudomonas syringae strains were isolated from 205 plant samples collected from apricot orchards showing symptoms of bacterial canker. As a consequence of the diagnostic tests, all isolates were identified as P. syringae pv. syringae. In this study, Pss, Psm R1 and Psm R2 strains in stone fruits were separated into different phylogroups (Pg‐2, Pg‐3, sPg‐1b) based on MLSA. Turkish strains obtained from stone fruits, particularly apricot, showed genetic heterogeneity, and clustered in different sub‐phylogroups (sPg‐2b, sPg‐2c, sPg‐2d). All these strains except strain K258 are also clustered in the same sub‐phylogroups (sPg‐2b and sPg‐2d) with other strains from different countries especially Iran, Lebanon, etc. Strain K258 isolated from apricot was clustered in sPg‐2c with Pss strain 642 (USA). The risk of bacterial canker disease in apricot growing areas is considered using GIS in this study. It was determined that a significant part of the Iğdır Plain, the biggest agricultural area in the Aras Basin, is at very high risk. [ABSTRACT FROM AUTHOR]

Intermediate wheatgrass (Thinopyrum intermedium; IWG) is a temperate perennial grass capable of performing in dual-purpose perennial grain cropping systems. It is valued for its ecosystem services and forage yield and quality that can be utilized in many livestock systems. Development for potential perennial grain yield has been the focus of breeding programs for more than two decades, and agronomic management is becoming important, as commercialization of the crop has occurred. This research focused on nitrogen management and intercropping on grain yield and yield components in western Canada. Treatments consisting of a non-fertilized control, an interseeded crop with IWG/Trifolium hybridum, and a fertilized treatment (50 kg N ha−1) were investigated at four locations. Drought conditions were experienced in some years, resulting in the loss of the interseeded crop at three locations. Fertilization with nitrogen increased grain yield in harvest years two and three and influenced yield components in at least one instance across locations. Third-year grain harvests were higher or equal to year one yield at the two locations harvested, with applied N increasing yield on average by 200 kg ha−1 in year three. Inflorescence density is an important yield component after the first production year. The potential for consistent grain yields across three reproductive years was demonstrated, enhancing the potential for sustained productivity. [ABSTRACT FROM AUTHOR]

Human activities all over the world cause many changes in the environment. These changes resulted in more complicated challenges facing all living things on earth. Among these challenges are abiotic stresses, particularly salinity and drought, which contribute to the huge crops yield loss around the world, thereby leading to food insecurity issues. Plants combat these adverse abiotic stress impacts via evolving a variety of physiological, biochemical and molecular mechanisms. Tremendous promising techniques are being used to enhance plant abiotic stress tolerance, including the applications of nanoparticles, which are thought to enhance plant performance under stress conditions. Nanoparticles are being used to deliver nutrients to plants, overcome plant diseases and pathogens and to sense and monitor trace elements present in soil. In this review, a glimpse of how nanoparticles could mitigate the negative impacts of salinity or drought stress on plants at the physiological, biochemical and molecular levels is elucidated and discussed. Understanding the underlying mechanisms of nanoparticles in abiotic stress tolerance will open the doors for further improvements in the yield, production and quality of main crops facing the changing environmental challenges. [ABSTRACT FROM AUTHOR]

Salt stress induces significant loss in crop yield worldwide. Although the growth‐stimulating effects of micronutrient nanoparticles (NPs) application under salinity have been studied, the molecular and biochemical mechanisms underlying these effects are poorly understood. The large size of maize leaf growth zones provides an ideal model system to sample and investigate the molecular and physiological bases of growth at subzonal resolution. Using kinematic analysis, our study indicated that salinity at 150 mM inhibited maize leaf growth by decreasing cell division and expansion in the meristem and elongation zones. Consistently, salinity downregulated cell cycle gene expression (wee1, mcm4, and cyclin‐B2‐4). B2O3 NP (BNP) mitigated the stress‐induced growth inhibition by reducing the decrease in cell division and expansion. BNP also enhanced the photosynthesis‐related parameters. Simultaneously, chlorophyll, phosphoenolpyruvate carboxylase and ribulose‐1,5‐bisphosphate carboxylase/oxygenase were stimulated in the mature zone. Concomitant with growth stimulation by BNP, mineral homeostasis, particularly for B and Ca, was monitored. BNP reduced oxidative stress (e.g., lessened H2O2 generation along the leaf zones and reduced lipid peroxidation in the mature zone) induced by salinity. This resulted from better maintenance of the redox status, that is, increased the glutathione‐ascorbate cycle in the meristem and elongation zones, and flavonoids and tocopherol levels in the mature zone. Our study has important implications for assessing the salinity stress impact mitigated by BNP on maize growth, providing a basis to improve the resilience of crop species under salinity stress conditions. [ABSTRACT FROM AUTHOR]

Balancing development and conservation is one of the major challenges for modern societies. Negative human–wildlife interactions, especially crop raiding by wildlife, greatly undermine this objective. Jilin Province in Northeast China encompasses ecosystems of high biodiversity value but endures serious losses in crop yields due to wild boars (Sus scrofa). Mitigation measures have been hindered by knowledge gaps in wild boar damage patterns. Our objective was to investigate how wild boar damage responded to human‐dominated landscapes, evaluating landscape patterns of damage at multiple ecological scales, thus providing a provincial map of damage risk to assist with damage mitigation. We used 1,144 coordinates of wild boar damage from 2009 to 2013 to train multi‐scale risk models with the maximum entropy algorithm. Damage risk was highest on gentle slopes and in heterogenous landscapes composed of fragmented plantations and cropland, with high edge density among small discontinuous patches, and with dense settlements in countryside areas with sporadic road networks. Scale had a substantial effect on relationships between damage and landscapes. We recommend mitigating the conflict risk by improving the compensation system and reducing the damage frequency through repelling, electric fencing, and dissuasive feeding. [ABSTRACT FROM AUTHOR]

In the Czech Republic, soil moisture content during the growing season has been decreasing over the past six decades, and drought events have become significantly more frequent. In 2003, 2015, 2018 and 2019, drought affected almost the entire country, with droughts in 2000, 2004, 2007, 2012, 2014 and 2017 having smaller extents but still severe intensities in some regions. The current methods of visiting cadastral areas (approximately 13,000) to allocate compensation funds for the crop yield losses caused by drought or aggregating the losses to district areas (approximately 1000 km 2 ) based on proxy data are both inappropriate. The former due to the required time and resources, the later due to low resolution, which leads to many falsely negative and falsely positive results. Therefore, the study presents a new method to combine ground survey, remotely sensed and model data for determining crop yield losses. The study shows that it is possible to estimate them at the cadastral area level in the Czech Republic and attribute those losses to drought. This can be done with remotely sensed vegetation, water stress and soil moisture conditions with modeled soil moisture anomalies coupled with near-real-time feedback from reporters and with crop status surveys. The newly developed approach allowed the achievement of a proportion of falsely positive errors of less than 10% (e.g., oat 2%, 8%; spring barley 4%, 3%; sugar beets 2%, 21%; and winter wheat 2%, 6% in years 2017, resp. 2018) and allowed for cutting the loss assessment time from eight months in 2017 to eight weeks in 2018. [ABSTRACT FROM AUTHOR]

Nitrogen (N), the most vital nutrient, undergoes a transformation in the soil and was subjected to loss through ammonia volatilization, nitrate leaching, and denitrification, which lower the soil fertility, crop yield and pollute the environment. Integrated nutrient management (INM) improves the nutrient supply system by integrating recommended dose fertilizers and biofertilizers, lessens the nitrogen losses, and synchronizes with the nutrient demand of the crop. To evaluate the impact of INM on minimization of N losses and improvement of wheat (Triticum aestivum L.) yield and growth parameters, a field experiment was conducted with different combinations of biofertilizers along with the recommended dosage of fertilizers. Application of NPK + Azotobacter + mycorrhiza (Tram) in combination was found to improve soil nitrogen dynamics, availability of P, K, microbial activity, root growth, urease activity, and nitrogen use efficiency as compared to NPK (Tr) only. The cumulative emission from ammonia flux in the INM plot varied from 10.2 to 10.6 kg ha−1 and denitrification losses varied from 2.4 to 2.5 kg ha−1. At the same time, these were recorded significantly higher in Tr plot. The yield obtained under the INM plot receiving Tram was recorded as 6.4 t ha−1, which is higher than other treatments. [ABSTRACT FROM AUTHOR]

Drought is a significant abiotic stress that negatively impacts plant growth and can lead to substantial crop yield losses. Our study aimed to establish selection criteria for 200 spring-wheat (Triticum aestivum L.) genotypes exposed to three different field-capacity (FC) levels (100%FC, 50%FC, 25%FC) in a growth chamber (seedling traits) and in the field under withholdirrigation conditions (yield traits). Variance, correlation, and principal component analysis (PCA) was performed to identify genotypes with high tolerance to drought stress. The stress induction significantly affects morphophysiological traits i.e., root length, root weight (fresh, dry), fresh shoot weight, relative water contents and root shoot ratio in spring wheat genotypes at seedling stage. The traits flag leaf area, spike weight, grain weight/spike, biological and grain yield were interlinked and strongly influenced by drought in the field. The mean grain yield performance decreased from 21.85 g to 19.46 g under variable water regimes; however, the differences in harvest index and spikelets/spike were not significant. Among the tested genotypes, 9970 and 36-Eswyt-42 showed better root architecture development, leaf water management system, biological yield (66.07 g, 56.23 g) and grain yield (27.47 g, 28.78 g), respectively. The traits we identified may serve as an indirect selection index to enhance current semi-arid wheat germplasm and develop high-yielding drought-tolerant lines in advanced generations. [ABSTRACT FROM AUTHOR]

The Pradhan Mantri Fasal Bima Yojana (PMFBY) is an area-based crop yield insurance scheme, which has been implemented in the state of Odisha in Eastern India since Kharif 2016. The study is based on a field survey conducted in the Bolangir district in Western Odisha. A total of 160 households comprising 80 loanee and 80 non-loanee PMFBY users were interviewed with predesigned questionnaires. This study identifies major risk factors confronted by the farmers in the study area and evaluates the effectiveness of PMFBY in addressing such risks. The farmers in the study area reported drought as the major risk factor that they face in agriculture. They consider adoption of crop insurance, off-farm employment and maintenance of credit reserves as important tools that had the most potential to manage risk. The study reveals that most of the farmers were dissatisfied with PMFBY due to delays in compensation payment, large loss assessing unit, and individual, independent risk not being covered under the scheme. For improving its operational efficiency, they put forth some important suggestions, such as: incorporating more cash crops under the scheme, individual assessment of crop loss and quick settlement of claims. [ABSTRACT FROM AUTHOR]

Precision agriculture plays a crucial role in optimizing crop yield, reducing environmental impact, and ensuring sustainable agricultural practices. Early detection and accurate diagnosis of leaf diseases are essential for preventing significant losses in crop production and maintaining food security. However, the inherent challenge of class imbalance in leaf disease datasets poses a significant obstacle for machine learning algorithms. In this paper, we explore and compare different techniques for handling class imbalance in leaf disease detection to improve the accuracy and reliability of machine learning models in the context of precision agriculture. We investigated the performance of different methods for leaf disease detection using the challenging New Plant Diseases Dataset (NPDD), which consists of image-based plant leaves. Our experiments reveal promising results, particularly with the hierarchical approach, achieving an accuracy of 97.17%. The outcomes of our study contribute to the growing body of knowledge in precision agriculture by providing a comprehensive analysis of techniques for handling class imbalance in leaf disease detection. Furthermore, our findings serve as a valuable resource for researchers and practitioners in the field, offering guidance on selecting and implementing the most effective approaches to tackle class imbalance challenges and improving the overall performance and reliability of machine learning models in the domain of precision agriculture. [ABSTRACT FROM AUTHOR]

Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection. [ABSTRACT FROM AUTHOR]

Following a request from the European Commission, the EFSA Panel on Plant Health performed a quantitative pest risk assessment of Elasmopalpus lignosellus (Lepidoptera: Pyralidae), the lesser cornstalk borer, for the EU. The assessment considered entry, establishment, spread and impact. Two scenarios for establishment were considered: (i) under current climatic conditions and (ii) under a future climate based on an ensemble of climate change scenarios. Impact assessment focused on cereal and legume host species. E. lignosellus is not known to occur outside of the Americas although it has been intercepted in the EU on fresh asparagus spears for consumption. Based on the size of the trade and evidence of interceptions, the importation of asparagus from Peru was identified as the most important pathway for entry. Using stochastic pathway modelling with parameter values based on Eurostat data and expert knowledge elicitation (EKE), the Panel estimated the median number of infested asparagus spears entering the EU annually to be approximately 8,600 (90% certainty range (CR) approximately 1,300–58,500). Each infested spear is likely to contain only one larva. Conditions are most suitable for establishment in the southern EU, especially around the Mediterranean basin. Under current climatic conditions, around 16% of spears enter regions of the EU suitable for establishment; this rises to 24% in the climate change scenario considered (2040–2059). However, due to estimated small likelihoods of adults emerging and escaping from discarded waste, finding a mate and the subsequent progeny surviving to initiate a founder population, the median number of populations expected to establish was estimated to be 0.0001 per year (90% CR 0.000005–0.002). Were E. lignosellus to establish, the median rate of natural spread was estimated to be 7.4 km/year (90% CR 0.6–18.2 km/year), after an initial lag period of 18.5 years (90% CR 3.3–43.8 years) following the establishment of a founder population. Estimated median yield losses in crops of cereals and legumes were estimated to be 0.95% (CR 0.2–2.8%), assuming farmers would adapt control measures such as are in place for other seedling pests. The Panel did not consider a scenario with additional risk reduction options because no feasible options at field level could be identified while export inspections aiming for zero contamination of the commodity are already in place in the exporting country – Peru. [ABSTRACT FROM AUTHOR]

The quantitative assessment of crop yield loss due to drought is critical for the development of the agricultural sector to improve the productivity. This study aimed to estimate and analyze the spatiotemporal patterns of crop yield loss in response to drought using the Lagrange interpolation method, wavelet analysis, and sequential Mann–Kendall test in Nepal's Koshi River Basin, covering the mountain, hill, and Terai (low-land) regions from 1987 to 2016. The findings indicate that average crop yield loss was common after 2000, with the Terai, hill, and mountain regions experiencing the greatest loss in maize, rice, and wheat crops, respectively. The average annual rice and wheat yield losses rate were highest in the mountains, while maize yield losses were highest in the Terai. The mountain region experienced an abrupt change in wheat yield loss, showing a significant increasing trend. The hill region showed a significant increase in maize and wheat yield loss, while a decrement in rice yield loss was observed. The periodic variations of maize, rice, and wheat between 1987 and 2016 revealed significant yield loss after 2000. The characteristics of the first and second key periods for crop yield loss demonstrated a variation period, predicting that crop yield loss would either enter a high yield loss or low yield loss period shortly after 2016. The study findings provide a detailed assessment of crop yield loss at the river basin level, highlighting the urgent need to develop a crop yield loss mitigation plan in the agricultural sector. [ABSTRACT FROM AUTHOR]