Biostimulants: A sufficiently effective tool for sustainable agriculture in the era of climate change?

Climate change is currently considered as one of the main concerns of the agriculture sector, as it limits crop production and quality. Furthermore, the current context of global crisis with international political instability and war conflicts over the world is pushing the agriculture sector even more to urgently boost productivity and yield and doing so in a sustainable way in the current frame of climate change. Biostimulants can be an effective tool in alleviating the negative effects of environmental stresses to which plants are exposed, such as drought, salinity, heavy metals and extreme temperatures etc. Biostimulants act through multiple mechanisms, modifying gene expression, metabolism and phytohormone production, promoting the accumulation of compatible solutes and antioxidants and mitigating oxidative stress. However, it is important to keep in mind that the use and effect of biostimulants has limitations and must be accompanied by other techniques to ensure crop yield and quality in the current frame of climate change, such as proper crop management and the use of other sustainable resources. Here, we will not only highlight the potential use of biostimulants to face future agricultural challenges, but also take a critical look at their limitations, underlining the importance of a broad vision of sustainable agriculture in the context of climate change.

Low risk management intervention: Limited impact of remedial tillage on net ecosystem carbon balance at a commercial Miscanthus plantation.

Perennial bioenergy crops are a key tool in decarbonizing global energy systems, but to ensure the efficient use of land resources, it is essential that yields and crop longevity are maximized. Remedial shallow surface tillage is being explored in commercial Miscanthus plantations as an approach to reinvigorate older crops and to rectify poor establishment, improving yields. There are posited links, however, between tillage and losses in soil carbon (C) via increased ecosystem C fluxes to the atmosphere. As Miscanthus is utilized as an energy crop, changes in field C fluxes need to be assessed as part of the C balance of the crop. Here, for the first time, we quantify the C impacts of remedial tillage at a mature commercial Miscanthus plantation in Lincolnshire, United Kingdom. Net ecosystem C production based on eddy covariance flux observations and exported yield totalled 12.16 Mg C ha-1 over the 4.6 year period after tillage, showing the site functioned as a net sink for atmospheric carbon dioxide (CO2). There was no indication of negative tillage induced impacts on soil C stocks, with no difference 3 years post tillage in the surface (0-30 cm) or deep (0-70 cm) soil C stocks between the tilled Miscanthus field and an adjacent paired untilled Miscanthus field. Comparison to historic samples showed surface soil C stocks increased by 11.16 ± 3.91 Mg C ha-1 between pre (October 2011) and post tillage sampling (November 2016). Within the period of the study, however, the tillage did not result in the increased yields necessary to "pay back" the tillage induced yield loss. Rather the crop was effectively re-established, with progressive yield increases over the study period, mirroring expectations of newly planted sites. The overall impacts of remedial tillage will depend therefore, on the longer-term impacts on crop longevity and yields.

Climate and disease: tackling coffee brown-eye spot with advanced forecasting models.

BACKGROUND: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables. RESULTS: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82). CONCLUSION: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry.

Drivers of ammonia volatilization in Mediterranean climate cropping systems.

Ammonia (NH3) volatilization is the major source of nitrogen (N) loss resulting from the application of synthetic and organic N fertilizers to croplands. It is well known that in Mediterranean cropping systems, there is a relationship between the intrinsic characteristics of the climate and nitrous oxide (N2O) emissions, but whether the same relation exists for NH3 emissions remains uncertain. Here, we estimated the impact of edaphoclimatic conditions (including meteorological conditions after N fertilization), crop management factors, and the measurement technique on both the cumulative emissions and the NH3 emission factor (EF) in Mediterranean climate zones, drawing on a database of 234 field treatments. We used a machine learning method, random forest (RF), to predict volatilization and ranked variables based on their importance in the prediction. Random forest had a good predictive power for the NH3 EF and cumulative emissions, with an R2 of 0.69 and 0.76, respectively. Nitrogen fertilization rate (N rate) was the top-ranked predictor variable, increasing NH3 emissions substantially when N rate was higher than 170 kg N ha-1. Soil pH was the most important edaphoclimatic variable, showing greater emissions (36.7 kg NH3 ha-1, EF = 19.3%) when pH was above 8.2. Crop type, fertilizer type, and N application method also affected NH3 emission patterns, while water management, mean precipitation, and soil texture were ranked low by the model. Our results show that intrinsic Mediterranean characteristics had only an indirect effect on NH3 emissions. For instance, relatively low N fertilization rates result in small NH3 emissions in rainfed areas, which occupy a very significant surface of Mediterranean agricultural land. Overall, N fertilization management is a key driver in reducing NH3 emissions, but additional field factors should be studied in future research to establish more robust abatement strategies.

Sustaining rice productivity through weather-resilient agricultural practices.

BACKGROUND: Enhancing productivity and profitability and reducing climatic risk are the major challenges for sustaining rice production. Extreme weather can have significant and varied effects on crops, influencing agricultural productivity, crop yields and food security. RESULTS: In this study, a comparative evaluation of two crop management systems was performed involving farmers adopting a weather forecast-based advisory service (WFBAS) and usual farmers' practice (FP). WFBAS crop management followed the generated weather forecast-based advice whereas the control farmers (FP) did not receive any weather forecast-based advice, rather following their usual rice cultivation practices. The results of the experiments revealed that WFBAS farmers had a significant yield advantage over FP farmers. With the WFBAS technology, the farmers used inputs judiciously, utilized the benefit of favorable weather and minimized the risk resulting from extreme weather events. As a result, besides the yield enhancement, WFBAS provided a scope to protect the environment with the minimum residual effect of fertilizer and pesticides. It also reduced the pressure on groundwater by ensuring efficient water management. Finally, the farmers benefited from higher income through yield enhancement, reduction of the costs of production and reduction of risk. CONCLUSION: A successful and extensive implementation of WFBAS in the rice production system would assist Bangladesh in achieving Sustainable Development Goal 2.4, which focuses on rice productivity and profitability of farmers as well as long-term food security of the country. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Mitigating life-cycle environmental impacts and increasing net ecosystem economic benefits via optimized fertilization combined with lime in pomelo production in Southeast China.

Excessive fertilization is acknowledged as a significant driver of heightened environmental pollution and soil acidification in agricultural production. Combining fertilizer optimization with soil acidity amendment can effectively achieve sustainable crop production in China, especially in Southeast China. However, there is a lack of long-term studies assessing the environmental and economic sustainability of combining fertilizer optimization with soil acidity amendment strategies, especially in fruit production. A four-year field experiment was conducted to explore pomelo yield, fruit quality, and environmental and economic performance in three treatments, e.g., local farmer practices (FP), optimized NPK fertilizer application (OPT), and OPT with lime (OPT+L). The results showed that the OPT+L treatment exhibited the highest pomelo yield and fruit quality among the three treatments. The OPT treatment had the lowest net greenhouse gas (GHG) emissions among the three treatments, which were 90.1 % and 42.6 % lower than those in FP and OPT+L, respectively. It is essential to note that GHG emissions associated with lime production constitute 40.7 % of the total emissions from fertilizer production. The OPT+L treatment reduced reactive nitrogen (Nr) emissions and phosphorus (P) losses, compared to FP and OPT. Moreover, the OPT+L treatment increased the net ecosystem economic benefit by 220.3 % and 20.3 % compared with the FP and OPT treatments, respectively. Overall, the OPT and OPT+L treatments underscore the potential to achieve environmentally friendly and economically sustainable pomelo production. Our study provides science-based evidence to achieve better environmental and economic performance in pomelo production through optimized NPK fertilization and alleviating soil acidification by lime.

Upland rice varietal mixtures in Madagascar: evaluating the effects of varietal interaction on crop performance.

Introduction: Rice plays a critical role in human livelihoods and food security. However, its cultivation requires inputs that are not accessible to all farming communities and can have negative effects on ecosystems. simultaneously, ecological research demonstrates that biodiversity management within fields contributes to ecosystem functioning. Methods: This study aims to evaluate the mixture effect of four functionally distinct rice varieties in terms of characteristics and agronomic performance and their spatial arrangement on the upland rice performance in the highlands of Madagascar. The study was conducted during the 2021-2022 rainfall season at two close sites in Madagascar. Both site differ from each other's in soil properties and soil fertility management. The experimental design at each site included three modalities: i) plot composition, i.e., pure stand or binary mixture; ii) the balance between the varieties within a mixture; iii) and for the balanced mixture (50% of each variety), the spatial arrangement, i.e., row or checkerboard patterns. Data were collected on yields (grain and biomass), and resistance to Striga asiatica infestation, Pyricularia oryzea and bacterial leaf blight (BLB) caused by Xanthomonas oryzae-pv from each plot. Results and discussion: Varietal mixtures produced significantly higher grain and biomass yields, and significantly lower incidence of Pyricularia oryzea compared to pure stands. No significant differences were observed for BLB and striga infestation. These effects were influenced by site fertility, the less fertilized site showed stronger mixture effects with greater gains in grain yield (60%) and biomass yield (42%). The most unbalanced repartition (75% and 25% of each variety) showed the greatest mixture effect for grain yield at both sites, with a strong impact of the varietal identity within the plot. The mixture was most effective when EARLY_MUTANT_IAC_165 constituted 75% of the density associated with other varieties at 25% density. The assessment of the net effect ratio of disease, an index evaluating the mixture effect in disease reduction, indicated improved disease resistance in mixtures, regardless of site conditions. Our study in limited environments suggests that varietal mixtures can enhance rice productivity, especially in low-input situations. Further research is needed to understand the ecological mechanisms behind the positive mixture effect.

Influence of organic plant breeding on the rhizosphere microbiome of common bean (Phaseolus vulgaris L.).

Introduction: We now recognize that plant genotype affects the assembly of its microbiome, which in turn, affects essential plant functions. The production system for crop plants also influences the microbiome composition, and as a result, we would expect to find differences between conventional and organic production systems. Plant genotypes selected in an organic regime may host different microbiome assemblages than those selected in conventional environments. We aimed to address these questions using recombinant inbred populations of snap bean that differed in breeding history. Methods: Rhizosphere microbiomes of conventional and organic common beans (Phaseolus vulgaris L.) were characterized within a long-term organic research site. The fungal and bacterial communities were distinguished using pooled replications of 16S and ITS amplicon sequences, which originated from rhizosphere samples collected between flowering and pod set. Results: Bacterial communities significantly varied between organic and conventional breeding histories, while fungal communities varied between breeding histories and parentage. Within the organically-bred populations, a higher abundance of a plant-growth-promoting bacteria, Arthrobacter pokkalii, was identified. Conventionally-bred beans hosted a higher abundance of nitrogen-fixing bacteria that normally do not form functional nodules with common beans. Fungal communities in the organically derived beans included more arbuscular mycorrhizae, as well as several plant pathogens. Discussion: The results confirm that the breeding environment of crops can significantly alter the microbiome community composition of progeny. Characterizing changes in microbiome communities and the plant genes instrumental to these changes will provide essential information about how future breeding efforts may pursue microbiome manipulation.

Leguminous Seedborne Pathogens: Seed Health and Sustainable Crop Management.

Pulses have gained popularity over the past few decades due to their use as a source of protein in food and their favorable impact on soil fertility. Despite being essential to modern agriculture, these species face a number of challenges, such as agronomic crop management and threats from plant seed pathogens. This review's goal is to gather information on the distribution, symptomatology, biology, and host range of seedborne pathogens. Important diagnostic techniques are also discussed as a part of a successful process of seed health certification. Additionally, strategies for sustainable control are provided. Altogether, the data collected are suggested as basic criteria to set up a conscious laboratory approach.

Simulated nitrous oxide emissions from multiple agroecosystems in the U.S. Corn Belt using the modified SWAT-C model.

Agriculture is a major source of nitrous oxide (N2O) emissions into the atmosphere. However, assessing the impacts of agricultural conservation practices, land use change, and climate adaptation measures on N2O emissions at a large scale is a challenge for process-based model applications. Here, we integrated six N2O emission algorithms for the nitrification processes and seven N2O emission algorithms for the denitrification process into the Soil and Water Assessment Tool-Carbon (SWAT-C). We evaluated the different combinations of methods in simulating N2O emissions under corn (Zea mays L.) production systems with various conservation practices, including fertilization, tillage, and crop rotation (represented by 14 experimental treatments and 83 treatment-years) at five experimental sites across the U.S. Midwest. The SWAT-C model exhibited wide variability in simulating daily average N2O emissions across treatment-years with different method configurations, as indicated by the ranges of R2, NSE, and BIAS (0.04-0.68, -1.78-0.60, and -0.94-0.001, respectively). Our results indicate that the denitrification process has a stronger impact on N2O emissions than the nitrification process. The best performing N2O emission algorithms are those rooted in the CENTURY model, which considers soil pH and respiration effects that were overlooked by other algorithms. The optimal N2O emission algorithm explained about 63% of the variability of annual average N2O emissions, with NSE and BIAS of 0.60 and -0.033, respectively. The model can reasonably represent the impacts of agricultural conservation practices on N2O emissions. We anticipate that the improved SWAT-C model, with its flexible configurations and robust modeling and assessment capabilities, will provide a valuable tool for studying and managing N2O emissions from agroecosystems.

Relationships between soil ecosystem services in temperate annual field crops: A systematic review.

Agricultural soils provide multiple ecosystem services (ES) that can replace chemical inputs to support agricultural production. However, most arable cropping systems are managed with little concern for preserving ecological functions, which could reduce their ability to provide these ES. An increasing number of studies assess ES from agroecosystems, but analysis of multiple ES distinguishing relationships that may exist between them and between these ES and their drivers is lacking. Thus, we performed a systematic literature review of soil-based ES relationships, with a focus on temperate annual field crops. Forty relevant studies out of 870 were selected for the analysis. We created an original ontology of soil-based ES, based on the indicators used to assess them, to which we added soil-based negative impacts and biomass production (defined as a good) to combine the ES approach and the impact approach. We summarized each positive (synergy), negative (trade-off) or non-significant relationship in these studies, which were either quantitative or qualitative. We highlighted key relationships that have never been investigated in the corpus selected, such as relationships between C sequestration and physical soil quality regulation, soil erosion regulation or soil biodiversity. Relationships between biomass production and soil-based ES or impacts were investigated the most and were mainly non-significant. This suggests there are agroecological practices for which maximizing bundles of ES does not always decrease agricultural production. Relationships between soil biodiversity and soil-based ES were exclusively synergistic or non-significant. Summarizing effects of drivers of these relationships revealed that the three pillars of conservation agriculture - rotation diversification (with ley or legumes), soil coverage with cover crops and reduced tillage - and organic fertilization seem promising practices to help provide balanced bundles of ES and potentially reduce negative agronomic impacts. We highlighted potential trade-offs that should be consciously considered when adapting management strategies.

Effect of blended fertilizer rates and planting density on yield and yield components of Irish potato (Solanum tuberosum L.) at Gombora condition, Hadiya zone, Southern Ethiopia.

Potato is an important food and cash crop and it has high yielding potential in many parts of Ethiopia; however the yield of the crop is often constrained due to low and imbalanced rates of inorganic fertilizers and inappropriate spacing. The field experiment was conducted to determine the effect of five rates of blended fertilizer (0, 100, 150, 200, and 250) kg NPS ha-1 and intra-row planting spacing of 20 cm, 30 cm and 40 cm and laid out by randomized complete block design with three replication in a factorial arrangement. The analysis of variance revealed that, marketable tuber yield, total tuber yield, stem number per hill, total fresh mass, underground fresh and dry mass were significantly (P < 0.05) influenced by the interaction of Nitrogen, Phosphorous and Sulfur (NPS blended fertilizer) and intra-row spacing. The highest plant height (96.60 cm), highest marketable tuber yield (34.29 tha-1), highest total tuber yield (38.36 t ha-1) and highest total fresh biomass (1274.2 g plant-1) were recorded from NPS rate of 250 kg NPS ha-1 and intra-row spacing of 20 cm while the lowest recorded from control treatment in wider intra-row spacing (40 cm). Therefore, the application of 250 kg NPS ha-1 with the intra-row spacing of 20 cm can give an optimum tuber yield and it could be recommended for the production of potato in the study area.

Predicting spatiotemporal soil organic carbon responses to management using EPIC-IIASA meta-models.

The management of Soil Organic Carbon (SOC) is a critical component of both nature-based solutions for climate change mitigation and global food security. Agriculture has contributed substantially to a reduction in global SOC through cultivation, thus there has been renewed focus on management practices which minimize SOC losses and increase SOC gain as pathways towards maintaining healthy soils and reducing net greenhouse gas emissions. Mechanistic models are frequently used to aid in identifying these pathways due to their scalability and cost-effectiveness. Yet, they are often computationally costly and rely on input data that are often only available at coarse spatial resolutions. Herein, we build statistical meta-models of a multifactorial crop model in order to both (a) obtain a simplified model response and (b) explore the biophysical determinants of SOC responses to management and the geospatial heterogeneity of SOC dynamics across Europe. Using 5600 unique simulations of crop growth from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM) covering 86,000 simulation units across Europe, we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soil across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-explicit meta models are highly accurate (R2 = 0.97) representations of the full mechanistic model and can be used in lieu of the full EPIC-IIASA GAM model for the estimation of SOC responses to cropland management. Model stratification by means of climate and soil clustering improved the performance of the meta-models compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-models largely capture broad SOC dynamics such as the linear nature of SOC responses to residue application, yet they often underestimate the magnitude of SOC responses to management. Furthermore, we find notable differences between the results from the biophysically-specific models throughout Europe, which point to spatially-distinct SOC responses to management choices such as nitrogen fertilizer application rates and residue retention that illustrate the potential for these models to be used for future management applications. While more accurate input data, calibration, and validation will be needed to accurately predict SOC change, we demonstrate the use of our meta-models for biophysical cluster and field study scale analyses of broad SOC dynamics with basically zero fine-tuning of the models needed. This work provides a framework for simplifying large-scale agricultural models and identifies the opportunities for using these meta-models for assessing SOC responses to management at a variety of scales.

Computational molecular explanation of Soybean AHAS resistance from P197S mutation.

The first enzyme in the pathway involving branched-chain amino is acetohydroxyacid synthase (AHAS, E.C. 2.2.1.6), which is inhibited by five commercial herbicide families. In this work a computational study of a point mutation of Proline-197-Serine of the Soybean AHAS enzyme, which was obtained by mutagenesis, explains the latter's S197 resistance to the commonly used Chlorsulfuron. Using protein-ligand docking and large-scale sampling and distributions from AlphaFold-generated the resistant and susceptible soybean AHAS protein structure. The computational approach here is scaled to screen for mutation probabilities of protein binding sites, similar to screening compounds for potential hits in therapeutic design using the docking software. P197 and S197 AHAS structures were found to be different even if only one amino acid was changed. The non-specific distribution of bindings in the S197 cavity after the P197S change has been rigorously calculated by RMSD analysis that it would require x20 more concentrations to fill the P197 site by the same amount. There is no previously performed detailed chlorsulfuron soybean P197S AHAS binding calculation. In the herbicide site of AHAS, several amino acids interact - a computational study could elucidate the optimal choice of point mutations for herbicidal resistance either individually or collectively by mutations one at a time and analyzing the effects with a set of herbicides individually. With a computational approach, enzymes involved in crop research and development could be analyzed more quickly, enabling faster discovery and development of herbicides.

"How sweet are your strawberries?": Predicting sugariness using non-destructive and affordable hardware.

Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to obtain quality estimations in a consistent and non-destructive manner. The majority of research on fruit quality measurements analyzed fruits in the lab with uniform data collection. However, it is laborious and expensive to scale up to the level of the whole yield. The "harvest-first, analysis-second" method also comes too late to decide to adjust harvesting schedules. In this research, we validated our hypothesis of using in-field data acquirable via commodity hardware to obtain acceptable accuracies. The primary instance that the research concerns is the sugariness of strawberries, described by the juice's total soluble solid (TSS) content (unit: °Brix or Brix). We benchmarked the accuracy of strawberry Brix prediction using convolutional neural networks (CNN), variational autoencoders (VAE), principal component analysis (PCA), kernelized ridge regression (KRR), support vector regression (SVR), and multilayer perceptron (MLP), based on fusions of image data, environmental records, and plant load information, etc. Our results suggest that: (i) models trained by environment and plant load data can perform reliable prediction of aggregated Brix values, with the lowest RMSE at 0.59; (ii) using image data can further supplement the Brix predictions of individual fruits from (i), from 1.27 to as low up to 1.10, but they by themselves are not sufficiently reliable.

Understanding fatty acid composition and lipid profile of rapeseed oil in response to nitrogen management strategies.

The fatty acid composition of rapeseed seeds plays an important role in oil quality for human nutrition and a healthy diet. A deeper understanding of fatty acid composition and lipid profiles in response to different nitrogen managements is critical for producing healthier rapeseed oil for the human diet. The fatty acid composition and lipid profiles were characterized through targeted GC-MS and lipidomics analysis (UPLC-MS) in this study. The results showed that nitrogen management significantly altered the fatty acid composition, thereby influencing oil quality when it is used to maximize the seed yield of rapeseed. Several fatty acid components (particularly oleic acid, linoleic acid, and linolenic acid) decreased significantly with increasing N application rate. A total of 1212 differential lipids in response to different N levels in the two varieties were clearly identified, that can be categorized into five classes, including 815 glycerolipids (GLs), 195 glycerophospholipids (GPs), 155 sphingolipids (SPs), 32 sterols (STs), and 15 fatty acyls (FAs). These differential lipids are likely to participate in lipid metabolism and signal transduction. Co-expression lipid modules were determined, and the key lipids, such as triglyceride (20:0/16:0/16:0; 18:0/18:1/18:3; 8:0/11:3/18:1), were found to be strongly related to several predominant fatty acids such as oleic acid and linoleic acid. The results further imply that some identified lipids are involved with lipid metabolism and could affect the fatty acid composition, which provide a theoretical guidance for increasing seed oil in Brassica napus.

Occurrence, Distribution, and Management of Aphid-Transmitted Viruses in Cucurbits in Spain.

The effectiveness of pest and disease management in crops relies on knowledge about their presence and distribution in crop-producing areas. Aphids and whiteflies are among the main threats to vegetable crops since these hemipterans feed on plants, causing severe damage, and are also able to transmit a large number of devastating plant viral diseases. In particular, the widespread occurrence of aphid-transmitted viruses in cucurbit crops, along with the lack of effective control measures, makes surveillance programs and virus epidemiology necessary for providing sound advice and further integration into the management strategies that can ensure sustainable food production. This review describes the current presence and distribution of aphid-transmitted viruses in cucurbits in Spain, providing valuable epidemiological information, including symptom expressions of virus-infected plants for further surveillance and viral detection. We also provide an overview of the current measures for virus infection prevention and control strategies in cucurbits and indicate the need for further research and innovative strategies against aphid pests and their associated viral diseases.

Accelerating technical change through ICT: Evidence from a video-mediated extension experiment in Ethiopia.

Despite enthusiasm around applications of information and communications technologies (ICTs) to smallholder agriculture in many lower-income countries, there are still many questions on the effectiveness of ICT-based approaches. This study assesses the impacts of video-mediated agricultural extension service provision on farmers' adoption of improved agricultural technologies and practices in Ethiopia using data from a two-year randomized experiment. Our results show that the video-mediated extension approach significantly increases uptake of recommended technologies and practices by improving extension access and farmer knowledge. Specifically, we find that video-mediated extension reaches a wider audience than the government's conventional extension approach and leads to higher levels of farmer understanding and uptake of the subject technologies in those locations randomly assigned to the program. While our results also point to greater extension access and greater knowledge among female spouses in locations where both male and female spouses were targeted by the program, we do not find clear evidence that a more inclusive approach translates into higher uptake of the subject technologies. Finally, we find that the video-mediated approach becomes less costly as the scale of operation increases.