Search Results (33)

FLOWERING LOCUS T (FT) is a key integrator of flowering pathways. White lupin, a grain legume, encodes four FT homologs: LalbFTa1, LalbFTa2, LalbFTc1, and LalbFTc2. Widespread distribution of white lupin implies diverse phenological adaptations to contrasting ecosystems. Recent studies highlighted associations between FT indels and flowering regulation. Therefore, we surveyed the global white lupin collection for the presence of such indels and potential links to phenology. A panel of 626 white lupin genotypes, representing several European and African agro-climates, was phenotyped under a long-day photoperiod in a two-year study, showing up to 80 days of flowering time difference between early landraces from Eastern Mediterranean and late accessions from France, Madeira, the Canaries, Greece, Italy, and the Azores. As many as seventeen indel variants were identified for LalbFTc1, twelve for LalbFTa2, nine for LalbFTa1, and four for LalbFTc2, yielding roughly three hundred allelic combinations. Significant correlations with phenology were confirmed for one LalbFTa1 indel and twelve LalbFTc1 indels. A large, highly correlated LalbFTc1 indel was revealed to be conserved among all domesticated Old World lupins, carrying all FTc1-promoter candidate binding sites of the same major floral repressor, AGAMOUS-LIKE 15. A small LalbFTa1 indel, providing additional contribution to earliness, showed homology between white and yellow lupins. LalbFTc1 indel-based PCR markers revealed high discriminatory power towards early (PR_42a and PR_71b) or late (PR_58c, PR_36b, PR_80, and PR_60b) flowering. Full article

In this study, we propose an enhanced methodology for assessing drought conditions through the systematic categorization of Condition Monitoring Observer Reports (CMORs) from Missouri between 2018 and 2024. Our approach introduces a novel classification framework to categorize drought impacts—meteorological, agricultural, hydrological, and socioeconomic—and aligns the analysis with established United States Drought Monitor (USDM) severity classifications. To complement this framework, we incorporate the New Drought Index (NDI), a recently developed quantitative metric that integrates atmospheric anomalies. Brief consideration is also given to atmospheric blocking patterns, which influence drought development. Advanced text processing techniques are employed to bridge qualitative and quantitative insights. The findings underscore the importance of integrating observer insights, atmospheric processes, and advanced indices to refine drought monitoring, inform climate adaptation strategies, and support proactive resource management. Full article

Assessing runoff under changing land use/land cover (LULC) and climatic conditions is crucial for achieving effective and sustainable water resource management on a global scale. In this study, the focus was on runoff predictions across three diverse Indian watersheds—Wunna, Bharathapuzha, and Mahanadi—spanning distinct agro-climatic zones to capture varying climatic and hydrological complexities. The soil and water assessment (SWAT) tool was used to simulate future runoff influenced by LULC and climate change and to explore the related sustainability implications, including related challenges and proposing countermeasures through a sustainable action plan (SAP). The methodology integrated high-resolution satellite imagery, the cellular automata (CA)–Markov model for projecting LULC changes, and downscaled climate data under representative concentration pathways (RCPs) 4.5 and 8.5, representing moderate and extreme climate scenarios, respectively. SWAT model calibration and validation demonstrated reliable predictive accuracy, with the coefficient of determination values (R²) > 0.50 confirming the reliability of the SWAT model in simulating hydrological processes. The results indicated significant increases in surface runoff due to urbanization, reaching >1000 mm, 600 mm, and 400 mm in southern Bharathapuzha, southeastern Wunna, and northwestern Mahanadi, respectively, especially by 2040 under RCP 8.5. These findings indicate that water quality, agricultural productivity, and urban infrastructure may be threatened. The proposed SAP includes nature-based solutions, like wetland restoration, and climate-resilient strategies to mitigate adverse effects and partially achieve sustainable development goals (SDGs) related to clean water and climate action. This research provides a robust framework for sustainable watershed management in similar regions worldwide. Full article

The agricultural sector faces significant challenges worldwide due to climate change. The pressure exerted by altered thermal conditions drives the zonal shift for various cultivations. This study aims to analyze and present the spatiotemporal evolution of the growing degree days (GDD) index in the northern Mediterranean Basin (NMB). More specifically, this research presents the multiyear analysis of the GDD index, which is focused on a high-value vine cultivation derived from the E-OBS dataset. The investigated time period spans from 1969 to 2018, and the performed analysis indicates a broad shift/expansion in areas with GDDs exceeding 2000 heat units. This is present in traditional winemaker countries such as France and Italy. Still, it is also evident that there is a high positive change in countries such as Serbia, Bulgaria, and other Balkans countries. The findings may be helpful in the strategic planning of the agricultural sector in these countries or on a vinery scale. Full article

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought-monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America and to enhance the utility of drought-monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impact information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and its indicators are changing over time as the climate warms and by recommending ways to strengthen the use of indices and indicators in drought decision making. Full article

The empirical Revised Universal Soil Loss Equation (RUSLE) has been adapted to geographical information system (GIS) frameworks to study the spatial variability of soil erosion across landscapes and has also been used to estimate reservoir sedimentation. The literature presents contradictory results about the efficacy of using RUSLE in a GIS context for quantifying reservoir sedimentation, requiring further evaluation and validation of its estimates relative to measured reservoir sedimentation. Our primary objective was to determine if these contradictory results may be a function of the RUSLE’s inability to account for sediments derived from gullies, stream channels, or stream banks; the temporal variability of some of RUSLE’s empirically based factors such as the land cover/land management (C-) factor; and in some model renditions, the choice of value for the sediment delivery ratio (SDR). The usefulness of adjusting these estimates using a regional representative value of gully/stream bank sediment contributions was also assessed. High-spatial horizontal resolution (2 m) digital elevation models (DEMs) for 12 watersheds were used together with C-factor data for five representative years in a GIS-based RUSLE model that incorporates SDR within a sediment routing routine to study the impacts of choice of C-factor and SDR on reservoir sedimentation estimates. Choice of image date for developing C-factors was found to impact reservoir estimates. We also found that the value of SDR for some of the study watersheds would have to be unrealistically small to produce sedimentation estimates comparable to measured values. Estimates of reservoir sedimentation were comparable to measured data for 5 of the 12 watersheds, when the regionally based adjustment for gully/stream bank contributions was applied. However, differences remained large for the remaining seven watersheds. Statistical analysis revealed that certain combinations of geomorphic, pedologic, or topographic variables could be used to predict the degree of sediment underestimation with a significant and high level of correlation (0.72 < R² ≤ 0.99; p-value < 0.05). Our findings indicate that the level of agreement between GIS-based RUSLE estimates of reservoir sedimentation and measured values is a function of watershed characteristics; for example, the area-weighted soil erodibility (K-) factor of the soils within the watershed and stream channels, the stream entrenchment ratio and bank full depth, the percentage of the stream corridor having slopes ≥ 21°, and the width of the stream flood way as a percentage of the watershed area. Within the context of GIS, these metrics are easily obtained from digital elevation models and publicly available soils data and may be useful in prioritizing reservoirs’ assessments for function and safety. Full article

The summer of 2022 was very dry across Missouri and the surrounding regions including much of the Great Lakes, Midwest, and southern plains of the USA. A comparison of this summer to the dry summer of 2012 and the relatively wet summers of 2018 and 2021 was carried out using the National Centers for Environmental Prediction/National Centers for Atmospheric Research reanalysis, the Climate Prediction Center teleconnection indexes, and the blocking archive at the University of Missouri. The summer of 2022 was like that of 2012 which was characterized by a strong 500 hPa height anomaly centered over the western US and plains as well as very little blocking in the East Pacific. The summers of 2018 and 2021 were characterized by more zonal flow over the USA and more blocking in the East Pacific, similarly to the results of an earlier study. The teleconnection indexes for the prior spring and summer were largely similar for the two drier years and opposite for the wetter years. The surface conditions for the drier years were more similar while these were opposite for the wetter years. The integrated enstrophy (IE) used in earlier studies identified a change in the large-scale flow regime in early June 2022, which coincided with a decrease in the precipitation over the study region. However, one key difference was that the spring of 2022 was characterized by blocking more consistent with a wetter summer. This would have made the predictability of the drought of summer 2022 less certain. Full article

Dioscorea composita is a plant with historical recognition for the production of secondary metabolites of pharmaceutical importance, including diosgenin, and with great nutritional and ethnobotanical value in its center of origin (México and Central America). Furthermore, it is considered a promising therapeutic agent against cancer. Currently, México is one of the two most important countries producing this yam; however, climate change is altering the environmental conditions of its natural habits, threatening its preservation and productivity. This is why this research was focused on characterizing the eco-geography of D. composita and predicting its potential geographic distribution under climate change scenarios in México-Central America. A collection of 408 geo-referenced accessions was used to determine its climatic adaptation, ecological descriptors, and the current and future potential geographic distribution, which was modeled with the MaxEnt model through the Kuenm R-package. For future climate scenarios, an ensemble of the GCMs HadGEM-ES and CCSM4 was used. Results showed that D. composita adapts to warm and humid and very humid agro-climates and, the most contributing variables for its presence are annual and seasonal moisture availability indices, the seasonal photoperiod, annual thermal range, and Bio14 and Bio11. The current potential distribution (692,123 km²) of D. composita might decrease by the year 2050 RCP4.5 (365,680 km²) and might increase by 2050 under the scenario RCP8.5 (763,589 km²), showing this plant could be a good crop option for this climate change scenario. The findings obtained provide valuable information that will allow for the effective utilization of this plant, both in terms of developing new pharmaceutical products and implementing appropriate conservation strategies. Full article

Centella asiatica is well known for its miraculous therapeutic properties in various systems of traditional medicine across the world. However, significant variation in its pharmacological activities has been reported due to the unavailability of quality raw material and non-standardized formulations. A number of research papers have been published on the collection of C. asiatica plants from different regions for the identification of a suitable agroclimate with elite germplasms. Efforts have been made to standardize production and post-harvest practices for the availability of quality raw material with a high centelloside content. The ecological niche modeling approach revealed that the Indian subcontinent has high climatic suitability for the production of C. asiatica, and genotypes with a high content of centelloside were predominantly found in the Western Ghats, North East, Eastern Himalaya and Western Himalaya in India. Open cultivation of C. asiatica is more suitable in these agroclimatic zones in India. Cultivation under shade is also suitable in the plains of Central India. Hydroponic and tissue culture of C. asiatica has also been successfully established for the enhanced production of centelloside using supplements and elicitors such as sucrose, auxins, cytokinins, kinetin, methyl jasmonate, etc. Freeze drying has been identified as the most efficient post-harvest method for the high pharmacological activities of C. asiatica extracts. Full article

This study evaluated the genotype by environment interactions in the yield and nutraceutical traits of the orange-fleshed sweet potato (OFSP) storage root in different agro-climatic zones of northern Ethiopia. Five OFSP genotypes were cultivated at three different locations following a randomized complete block design, and the yield, dry matter, beta-carotene, flavonoids, polyphenols, soluble sugars, starch, soluble proteins, and free radical scavenging activity were measured in the storage root. The results showed consistent variations in the nutritional traits of the OFSP storage root depending on both the genotype and the location, as well as on their interaction. Ininda, Gloria, and Amelia were the genotypes that provided the higher yield and dry matter, as well as the higher content of starch and beta-carotene; they also showed a high antioxidant power. These findings suggest that the studied genotypes have the potential to alleviate vitamin A deficiency. This study demonstrated a high possibility of sweet potato production for storage root yield in arid agro-climate regions with limited production inputs. Moreover, the results suggest that it is possible to enhance the yield, dry matter content, beta-carotene, starch, and polyphenols of the OFSP storage root through genotype selection. Full article

Accurate yield information empowers farmers to adapt, their governments to adopt timely agricultural and food policy interventions, and the markets they supply to prepare for production shifts. Unfortunately, the most representative yield data in the US, provided by the US Department of Agriculture, National Agricultural Statistics Service (USDA-NASS) Surveys, are spatiotemporally patchy and inconsistent. This paper builds a more complete data product by examining the spatiotemporal efficacy of random forests (RF) in predicting county-level yields of corn—the most widely cultivated crop in the US. To meet our objective, we compare RF cross-validated prediction accuracy using several combinations of explanatory variables. We also utilize variable importance measures and partial dependence plots to compare and contextualize how key variables interact with corn yield. Results suggest that RF predicts US corn yields well using a relatively small subset of climate variables along with year and geographical location (RMSE = 17.1 bushels/acre (1.2 tons/hectare)). Of note is the insensitivity of RF prediction accuracy when removing variables traditionally thought to be predictive of yield or variables flagged as important by RF variable importance measures. Understanding what variables are needed to accurately predict corn yields provides a template for applying machine learning approaches to estimate county-level yields for other US crops. Full article

The properties of soil constitute one of the most important features of the environment that determine the potential for food production in a given region. Knowledge of the soil texture and agroclimate allows for the proper selection of species and agrotechnics in plant production. However, in contrast to the agroclimate, the soil may show a large spatial variation of physical and chemical characteristics within the plot. In regions where the soil diversity is so high that the available soil maps are not sufficient, the only method that allows for precise mapping of the soil mosaic is remote sensing. This paper presents the concepts of using Sentinel-2 multispectral satellite images to detail the available soil-agriculture map at a scale of 1:25,000. In the presented work, the following research hypothesis has been formulated: spatial and temporal analysis of high-resolution satellite images can be used to improve the quality of a large-scale archival soil-agriculture map. It is possible due to the spatial differentiation of the spectral reflection from the field (canopy), which is influenced by soil conditions—especially the differentiation of physical properties (granulometric composition) in soil profiles which determine the possibility of water retention during drought conditions. The research carried out as a case study of maize remote sensing confirmed the hypothesis. It was based on the selection of the most appropriate term (maize development period: BBCH 79, 6-decade drought index: CBW = −206 mm) and the vegetation index (NDVI). This made it possible to make the scale of the map 10 times more detailed. The obtained results are the first step in developing a general model (based on remote sensing) for detailing the soil-agriculture map for Poland, which will significantly improve the accuracy of the drought monitoring system developed by the Institute of Soil Science and Plant Cultivation (Poland). Full article

Rice (Oryza sativa L.) grain yield is reduced by drought stress during reproductive stages. In this work, the effects of foliar selenium (Se) spray on the reduction of drought stress are measured and the most critical stage of rice is identified. Three field experiments were carried out. In experiments I and II, the sensitive stage was determined by imposing drought stress at the panicle initiation and flowering stages by withholding irrigation for 10, 15, 20, and 25 days. In experiment III, drought stress was induced by depriving plants of moisture for 20 or 25 days. Se spray was then applied at rates of 0, 10, and 20 mg L⁻¹ to alleviate the drought, and its effects were evaluated. Overall, it was reported that the panicle initiation stage was more vulnerable to drought stress than the flowering stage, as evidenced by a larger reduction in grain yield. Employing Se as a foliar spray at a rate of 20 mg L⁻¹ under drought stress enhanced the number of filled grains m⁻², the total number of grains m⁻², and grain yield ha⁻¹ by 22.0, 4.3, and 11.0%, respectively, over water spray. This shows a positive interaction or synergistic interaction between drought tolerance and selenium application. These results demonstrate that foliage supplementation of Se at a dosage of 20 mg L⁻¹ alleviate the adverse effects of drought stress at the panicle initiation stage in rice cultivation. Se mitigated the negative effects of drought by improving the membrane stability index, relative water content, and proline content. Full article

The water footprint (WF), based on irrigation water quality, is important as a decision-making tool for crop selection based on the comparative advantage of water consumption and yield to maximize agricultural water productivity and sustainably improve water use efficiency. This paper presents a generic link between climate variability and water footprint. To support this link, a case study is presented for wheat and maize in different agro-climate zones in Egypt. In this study, the three agro-ecological zones, Nile Delta, Middle Egypt, and Upper Egypt, were selected to represent three different microclimates. The climate data were analyzed to estimate reference evapotranspiration (ETo) and calculate crop water use (CWU) for wheat and maize from 2015 through 2019. Cultivated area and yield data were analyzed during the study period. Water footprint (WF) was calculated for old land (clay soils) and new lands (sandy soils) in three climate regions based on blue and grey water. Green water was excluded due to negligible rainfall depths in Egypt. The results showed that the mean values of WF for maize were 1067, 1395, 1655 m³/ton in old land and 1395, 1634, 2232 m³/ton in new land under the three climate regions, respectively, while it was 923, 982, 1117 m³/ton in old land and 1180, 1258, 1452 m³/ton for wheat in new land for the three regions, respectively. The results show that the crop water use fluctuated over regions due to climate variability where the CWU values were 6211, 7335, 8007 m³/ha for maize and 4348, 4825, 5774 m³/ha for wheat in the three regions, respectively. The results show an 11% and 33% increase in maize and an 18% and 29% increase in wheat CWU in Middle and Upper Egypt regions comparing to what was observed in Nile Delta due to an increase in solar radiation, temperature, and wind speed. The Egypt mean value of wheat water footprint was 1152 m³/ton and mean value of maize water footprint was 1563 m³/ton. The data clearly show the effect of microclimate variability on WF and irrigation requirements between regions. The methodology and results from this study provide a pathway to help the policy makers to mitigate climate change impacts on crop yield and to enhance water resources management in major crop production regions by redistribution of the cropping patterns based on the comparative advantages of each crop within each region. The crop choices relative to the soil water retention characteristics could also contribute to the moderation of microclimate, which affects ETo and ETc and the water footprint. Full article

Yellow mosaic disease (YMD), incited by mungbean yellow mosaic virus (MYMV), is a primary viral disease that reduces mungbean production in South Asia, especially in India. There is no detailed knowledge regarding the genes and molecular mechanisms conferring resistance of mungbean to MYMV. Therefore, disclosing the genetic and molecular bases related to MYMV resistance helps to develop the mungbean genotypes with MYMV resistance. In this study, transcriptomes of mungbean genotypes, VGGRU-1 (resistant) and VRM (Gg) 1 (susceptible) infected with MYMV were compared to those of uninfected controls. The number of differentially expressed genes (DEGs) in the resistant and susceptible genotypes was 896 and 506, respectively. Among them, 275 DEGs were common between the resistant and susceptible genotypes. Functional annotation of DEGs revealed that the DEGs belonged to the following categories defense and pathogenesis, receptor-like kinases; serine/threonine protein kinases, hormone signaling, transcription factors, and chaperons, and secondary metabolites. Further, we have confirmed the expression pattern of several DEGs by quantitative real-time PCR (qRT-PCR) analysis. Collectively, the information obtained in this study unveils the new insights into characterizing the MYMV resistance and paved the way for breeding MYMV resistant mungbean in the future. Full article