http://hdl.handle.net/10366/130155 2026-04-24T07:23:10Z http://hdl.handle.net/10366/166866 [EN] Colletotrichum truncatum, the predominant fungal species associated with soybean anthracnose, is responsible for significant losses in this crop. Chemical control via fungicide application is the most effective strategy for the control of soybean foliar diseases. However, the increasing incidence of anthracnose in some regions of Brazil indicates that current chemical control is not effective against anthracnose. In this study, we evaluated the fungicide sensitivity of C. truncatum genetic lineages to the fungicides azoxystrobin, thiophanate-methyl, difenoconazole, and fludioxonil using isolates representing two important regions of soybean production in Brazil. We characterized the molecular resistance to the quinone-outside inhibitors (QoI), methyl benzimidazole carbamates (MBC), and demethylation inhibitor (DMI) fungicide groups based on amino acid sequences of the cytochrome b (cytb), β-tubulin gene (β-tub), and P450 sterol 14a-demethylases (CYP51) genes. Multiple resistance of C. truncatum isolates to QoI and MBC was observed associated with mutation points in the β-tub (E198A and F200Y) and cytb (G143A). Alternatively, low EC50 values were found for fludioxonil and difenoconazole indicating high efficacy. Analysis of C. truncatum genomes revealed two potential DMI targets, CYP51A and CYP51B, and higher genetic variability in the CYP51A gene. A positive correlation was found between genetic differentiation of C. truncatum populations and fungicide sensitivity (Student's t-test <0.001). To our knowledge, this is the first report of multiple resistance to QoI and MBC fungicides in C. truncatum in Brazil. 2024-01-01T00:00:00Z http://hdl.handle.net/10366/161334 The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-meansquare error<0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions.The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations,performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zoneSMwith complete spatio–temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions. 2022-11-02T00:00:00Z http://hdl.handle.net/10366/160846 [EN] Soil moisture (SM) plays a key role in the water cycle, and its variability is intimately linked to coupled landatmosphere processes. Having a good knowledge of soil-atmospheric interactions is thus essential to assess the impact of climate change on SM; however,many aspects of howwater and energy exchanges occur in the soil-atmosphere continuum are still uncertain. In particular, it is known that atmospheric circulation patterns influence climate conditions over Europe but their impact on SM has only rarely been studied. This study provides insight into how atmospheric patterns influence soil moisture dynamics in Europe, where an increase in temperature and agricultural droughts are expected as an impact of climate change. To do so, we analysed the influence of the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), and the El Niño Southern Oscillation (ENSO) on European SM, including lagged responses, for the period 1991–2020 at a monthly scale. Two methods have been used: a lagged correlation analysis and a more sophisticated causality approach using the PCMCI (PC method combined with themomentary conditional independence (MCI) test). SMseries fromtwo different databases were considered: the hydrological model LISFLOOD and the reanalysis dataset ERA5-Land. The results from the correlation analysis showed a significant, predominantly negative relationships of SM with NAO and AO over almost all of Europe and no significant relation with ENSO. With the causality analysis, similar patterns are obtained for NAO and AO; however, the PCMCI analysis revealed clear patterns of ENSO influencing SM with a delayed response of one-to-two months in central and northwest Europe. The results obtained in this work highlight that there are causal relations between the main modes of interannual climate oscillations and SM variations in Europe, underlining the importance of accounting for global atmospheric circulations to study current changes in regional soil water-related processes 2022-01-01T00:00:00Z http://hdl.handle.net/10366/160651 [EN]Study region: The Henares River Basin, central part of Spain Study focus: An assessment of SWAT soil moisture in a basin under Mediterranean conditions was carried out for the first time. Intercomparison analysis was performed using modeling (LISFLOOD model) and remote sensing (Soil Moisture and Ocean Salinity, SMOS; and Soil Moisture Active Passive, SMAP) products as references, both in the topsoil and in the root zone layers. The analysis was carried out considering both the temporal and the spatial domains. New hydrological insights for the region: The evaluation results, despite being slightly better for the root zone layer, were very good overall in terms of the correlation coefficient, centered root mean square difference, and bias. The SWAT soil moisture product obtained in a basin under Mediterranean conditions gave satisfactory performances, similar to those of the modelling and satellite products with which it has been compared. When analyzing whether these assessment results were determined by factors such as land cover, soil characteristics (texture), topography (slope and altitude) and climate (precipitation), no pattern was observed. There was also no temporal pattern in the quality of the SWAT soil moisture estimation under Mediterranean conditions. Producing reliable SWAT soil moisture databases for different environments around the world, offers the possibility of increasing the availability of suitable soil moisture series, for which there is great interest. 2023-06-28T00:00:00Z http://hdl.handle.net/10366/160650 [EN]Rainfed cereal yields show high variability depending on the varying conditions of concurrent factors during the crop year. Among them, hydrometeorological factors such as maximum temperature, rainfall, and notably, soil moisture, strongly affect crop production, but the greatest source of uncertainty on yield estimates stems from their interaction. This is of special interest in water-limited regions where climate change is expected to affect more intensely, but also in others where water is increasingly limited. Despite the highly non-linear nature of the interactions, simple statistic models such as multilinear regression accurately explore a notable proportion of the variability of cereal yields. To describe the impacts behind interactions, we perform stepwise multilinear regression of meteorological factors derived from E-OBSv23 database and soil moisture from ERA5-Land against annual wheat and barley yields for the period 1981–2019 in the main cereal regions of Spain. The multivariate approach characterizes the temporal shifts of factors’ influence. Beyond the temporal shifts on the synchrony of the factors, some of them tend to co-dominate the impact during the critical period of crop development, with soil moisture exceeding all others in relevance. Multivariate analysis fosters discussion about the impact of the choice of variables on the model fit, as well as on the pertinence of monthly and annual scales for explorative and predictive purposes. Monthly models perform particularly well during the critical period of growth and reproduction of crops and consistently better than univariate estimates. The annual model built using the data of the months of maximum impact of key variables outperforms the model at a monthly scale, which underlines the decisive role of the critical period. Similarly, results highlight the worth of parsimony in modelling. Soil moisture stands out as the principal concurrent variable to improve yield estimates from environmental data, which governs yields of rainfed water-limited croplands. 2023-03-27T00:00:00Z http://hdl.handle.net/10366/160649 [EN]In this research, the active, passive and combined Climate Change Initiative (CCI) Soil Moisture (SM) products were evaluated in comparison with in situ SM measurements from five networks in Spain that have different spatial and temporal scales, densities and environmental conditions. Three of these networks, namely Rinconada, Morille and the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), are small- to medium-scale networks and have high station densities, whereas the other two (Inforiego and FluxNet) are sparse and large-scale networks. The results of the comparisons with the former v02.2 version (before the inclusion of the SM retrieved by the Soil Moisture and Ocean Salinity mission, SMOS, in the CCI dataset) showed that the combined CCI performed better than the active or passive, affording correlation coefficients (R) above 0.8 and errors between 0.03 and 0.08 m3 m−3 for the area-average, with biases close to zero. Regarding the land uses and environmental conditions, the stations that were located in the agricultural areas and some forested areas showed the best results, and those that were located in pasture and certain specific agricultural locations showed the poorest results. To test the opportunity of including SMOS in CCI, both datasets were compared over the same areas and coincident periods. After the results, the combined CCI and SMOS SM products matched very well (R=0.83 on average), although the SMOS and CCI under- and overestimate the ground soil moisture measurements, respectively. Finally, the new version of the combined CCI (v03.2, after including SMOS) showed similar correlations to the previous one, but it significantly reduced the bias, leading to slightly lower errors (RMSD and cRMSD). Hence, it was shown that including SMOS in the CCI database enhanced its performance. The results in this work may improve knowledge of the CCI SM and its potential applications. 2019-04-24T00:00:00Z http://hdl.handle.net/10366/160648 [EN]In this study, the ability of satellite soil moisture to track tree growth is analyzed. Despite the reticence of using satellite soil moisture data in forest areas, there is evidence that in some cases, good and reliable results have been obtained. Forests cover very important continental areas and have great importance in many natural processes related to water resources, biodiversity and climate. For these reasons, it is interesting to investigate the applicability of remote sensing soil moisture products for new applications in these environments. In the present study, the CCI (Climate Change Initiative, Global Monitoring of Essential Climate Variables, European Space Agency) soil moisture (CCISM) database has been correlated with the tree-growth series of 22 samples of Aleppo pine (Pinus halepensis Mill.) in Spain in different locations and environmental conditions. Aleppo pine is the most widespread pine species in the Mediterranean basin, and therefore, it is highly representative of the bioclimatic conditions in these water-limited environments. The series spans from 1978 to 2016, and the daily CCISM has been correlated with the annual tree-growth anomalies. The daily CCISM has been obtained using a moving window scheme with 1-, 7-, 15- and 30-day averages, starting on October 1st of the previous year and finishing on December 31st of the corresponding year. Another soil moisture product (Lisflood model) and precipitation have also been used in a similar approach to strengthen the assessment analysis. The results obtained show a clear temporal pattern of the relationship between satellite soil moisture and Aleppo pine tree growth, and the influence of soil moisture on tree-growth dynamics increases with reduced water availability. The CCISM was able to detect a bimodal pattern of tree growth with a maximum in May and a secondary peak in autumn. This temporal pattern was much clearer than that obtained using the modeled soil moisture and the precipitation. This study proves that satellite soil moisture is sensitive enough to track the phenology of this forest species. The results obtained demonstrate that satellite soil moisture data could be suitable for use in forest environments and for new applications. 2019-10-31T00:00:00Z http://hdl.handle.net/10366/160647 [EN]It is of primary interest to determine the extent that natural processes are linked to understand natural system dynamics. There is growing interest in investigating the coupling and coevolution occurring between natural processes and the interaction between natural systems. Soil moisture is a variable that is at the core of the interaction between multiple physical and biological processes; therefore, it is very important to understand the influence of soil moisture dynamics on that coevolution. Soil moisture memory (SMM) shows soil moisture dynamics and the nature of their interaction with other related processes. Several studies have recognized that SMM depends on soil properties and some other meteorological andbiophysical variables; however, there has not been a study specifically devoted to studying the relationshipbetween SMM and the main soil properties. The present study analyzes the relationship between several relevant soil properties (texture, bulk density and organic matter content) involved in soil water dynamics and the stored precipitation fraction (Fp) as a suitable expression of the SMM. The relationship between SMM and precipitation and soil water content was also analyzed. These relationships were analyzed both at the surface layer and at several soil layer depths and with four soil moisture sampling frequencies (3 and 12 h, and 1 and 3 days). The study was conducted with data collected over a period of 12 years, at two soil moisture monitoring networks (REMEDHUS and SCAN) located in Spain and the USA, respectively, covering a wide range of soil characteristics and environmental conditions. The results showed that soil texture plays the main role in understanding SMM dynamics and that organic matter content has a good relationship with Fp. The sand fraction is the variable more correlated with Fp, with a clear inverse relationship (correlation coefficient, from -0.62 to -0.92 in REMEDHUS, and from -0.65 to -0.76 in SCAN). The variations in Fp observed with the different soil layer depths are also mainly correlated with texture and organic matter content, but only for sampling intervals of 1 and 3 days. The temporal variability of Fp in the different soil layers is more correlated with the soil water content than with annual precipitation, especially in the surface layer. At 0–5 depth, R ranges between 0.42 and 0.82. For sampling intervals of 1 and 3 days, the R values between Fp and mean soil moisture are always significant (p < 0.01) and higher than 0.66 in all soil layers. Therefore, in view of the cases studied and the results obtained, the most critical aspect of understanding SMM dynamics is precise characterization of the water storage conditions, especially in terms of texture and organic matter content. 2021-01-25T00:00:00Z http://hdl.handle.net/10366/160646 [EN]Soil moisture (SM) plays a key role in the water cycle, and its variability is intimately linked to coupled landatmosphere processes. Having a good knowledge of soil-atmospheric interactions is thus essential to assess the impact of climate change onSM; however,many aspects of howwater and energy exchanges occur in the soil-atmosphere continuum are still uncertain. In particular, it is known that atmospheric circulation patterns influence climate conditions over Europe but their impact on SM has only rarely been studied. This study provides insight into how atmospheric patterns influence soil moisture dynamics in Europe, where an increase in temperature and agricultural droughts are expected as an impact of climate change. To do so, we analysed the influence of the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), and the El Niño Southern Oscillation (ENSO) on European SM, including lagged responses, for the period 1991–2020 at a monthly scale. Two methods have been used: a lagged correlation analysis and a more sophisticated causality approach using the PCMCI (PC method combined with the momentary conditional independence (MCI) test). SM series from two different databases were considered: the hydrological model LISFLOOD and the reanalysis dataset ERA5-Land. The results from the correlation analysis showed a significant, predominantly negative relationships of SM with NAO and AO over almost all of Europe and no significant relation with ENSO. With the causality analysis, similar patterns are obtained for NAO and AO; however, the PCMCI analysis revealed clear patterns of ENSO influencing SM with a delayed response of one-to-two months in central and northwest Europe. The results obtained in this work highlight that there are causal relations between the main modes of interannual climate oscillations and SM variations in Europe, underlining the importance of accounting for global atmospheric circulations to study current changes in regional soil water-related processes. 2022-07-22T00:00:00Z http://hdl.handle.net/10366/160558 [EN]This study investigates the relationship between soil moisture and the growth of Pinus halepensis, P. nigra, P. sylvestris and P. uncinata, which are some of the main pine species of the Iberian Peninsula, and the response of these species to soil drought. The role played by climatic and geographic factors in the resilience of these species to drought events is also evaluated. A total of 110 locations of the four species studied were selected, with data ranging from 1950 to 2007. The results show that the species that are less dependent on soil moisture best withstood droughts, while those more dependent on it showed better adaptability. Additionally, climatic and geographic factors had a stronger influence on the species’ resilience to soil drought at higher altitudes. The results of this study can help us to better understand forest ecosystem dynamics and their reaction to droughts in Mediterranean areas, where this phenomenon will be much more severe in the future due to climate change. 2023-07-26T00:00:00Z http://hdl.handle.net/10366/160555 [EN]In countries where the economy relies mostly on agricultural-livestock activities, such as Argentina, droughts cause significant economic losses. Currently, the most-used drought indices by theArgentinian National Meteorological and Hydrological Services are based on field precipitation data, such as the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). In this article, we explored the performance of the satellite-based soil moisture agricultural drought index (SMADI) for agricultural drought detection in Argentina during 2010-2015, and compared it with the one from the standardized soil moisture anomalies (SSMA), SPI and SPEI (at one-month and three-month temporal scales), using the AgriculturalMinistry’s drought emergency database as a benchmark. The performances were analyzed in terms of the suitability of each index to be included in an early warning system for agricultural droughts, including true positive rate (TPR), and both false positive and false negative rates. In our experiments, SMADI showed the best overall performance, with the highest TPR and F1-score, and the second best false positive rate (FPR), positive predictive value, and overall accuracy. SMADI also showed the largest difference between TPR and FPR. SSMA showed the lowest FPR, but also the lowest TPR, making it not useful for an alert system. Furthermore, field precipitation-based indices, yet simple and widely used, showed not to be suitable indicators for detection of agricultural drought for Argentina, neither in the one-month nor in the three-month scale. 2021-05-28T00:00:00Z http://hdl.handle.net/10366/160486 [EN]The relationship between modelled root zone soil moisture (SM) and black truffle production in the Iberian Peninsula was studied. Previous works have investigated the influence that precipitation exerts on truffle yield highlighting the importance of water for the growth of black truffle. However, SM had not been used until now due to the lack of suitable databases. The SM series from the LISFLOOD hydrological rainfall–runoff model was used in this study. Annual black truffle yield series from 175 locations in Spain was correlated with SM for the period 1991–2012. For this, different approaches were applied considering daily, weekly and monthly temporal scales. The same analysis was carried out using precipitation data to compare the behaviors of both variables related to truffle production variability. The results obtained show critical periods in terms of soil water content in summer (June–September) and during October–November months. Moreover, a clear delay between precipitation and SM influence on black truffle was observed. The results obtained in this study highlight the importance of SM for black truffle production, since this variable truly expresses the available water for this fungus, which completes its entire life cycle living below ground. 2022-05-24T00:00:00Z http://hdl.handle.net/10366/160442 [EN]Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, which is the longest available satellite-based SM data record. The methods used were linear, cubic and autoregressive interpolation and support vector machines (SVMs). This study focused on Southern Europe and spanned the years 2003–2015. The different methods were applied in the temporal and spatial domains and evaluated using the holdout cross-validation technique. A set of variables was introduced in the SVM model to estimate SM, namely, land surface temperature, precipitation, normalized difference vegetation index (NDVI), potential evaporation, soil texture and geographical coordinates. For the SVMs, several combinations of these variables were considered, including a principal component analysis (PCA) containing all of them. Although the different methods show a generally good performance, the SVM method outperforms the rest. Using the SM of the precedent day (SMt-1) is key to obtain good estimates. The median value of the correlation coefficient (R) obtained with the SVM and the SMt-1 series in the temporal analysis was 0.83, and the RMSE was 0.025 m3 m-3. Similar results were obtained in the spatial analysis, with the best performance (R = 0.88; RMSE = 0.024 m3 m- 3) obtained by the SVM using the SMt-1 series and the static variables. The application of PCA to input variables was not beneficial, and the interpolation methods failed when dealing with large spatial or temporal gaps. A validation of the CCI SM series with in situ SM data from four networks located in Spain, France, Germany and Italy was also performed and no substantial differences were observed between results obtained with the original and with the reconstructed series. In addition, best inputs obtained with SVM were used to evaluate the random forest (RF) method in the temporal and spatial domain. This method showed a good ability to estimate soil moisture values in the temporal domain but to a lesser extent than SVM while for the spatial domain it did not seem to be as accurate. Our results confirm that we can efficiently deal with spatio-temporal gaps on observational SM databases using the SVM method and the past time series and soil texture as supporting information. 2021-03-03T00:00:00Z http://hdl.handle.net/10366/160415 [EN]Given the growing interest in drought impacts on crops, this work studied the impact of agricultural drought on wheat and barley during the period 2001–2020. The study was carried out in the Spanish regions of Castilla y León and Castilla–La Mancha, with approximate areas of 94,000 km2 and 79,000 km2, respectively, and in the German regions of Nordrhein-Westfalen, Niedersachsen and Bayern, with approximate areas of 34,000 km2, 48,000 km2 and 71,000 km2, respectively. These are the main cereal-growing regions of Spain and Germany. Soil moisture (SM) in the root zone was extracted from the LISFLOOD model database, and SM anomalies were used as the agricultural drought index. Gross primary productivity (GPP) and leaf area index (LAI) variables were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the month in which SM is most influential on these crop state variables was identified. Crop yields in Spain and Germany were obtained from the Spanish Ministry of Agriculture, Fisheries and Food and the German Federal Statistical Office, respectively. Agricultural drought years and their impact on cereal yields were determined on a regional scale using three approaches based on the critical month with different time periods. These approaches were the use of the critical month and the two (before or after) and the three months (before and after) around the critical month. Two different analyses were used to identify the critical month, depending on the different environmental conditions in each country. These two approaches consisted of a monthly correlation analysis between SM anomalies and cereal yield in Spain and a monthly trend analysis of SM anomalies in Germany. The results showed a dependence of crop variables on SM in spring months in both countries and in summer months in Germany. Differences were found depending on the environmental conditions. A considerable reduction in cereal yields was obtained in Spain which exceeded 30%. Similarly, a worrying sign was observed in Germany, with a positive agricultural drought trend and a yield reduction of almost 5% in cereal crops. In view of future forecasts of the negative impact of climate change on global food production, this study provides valuable information for water and agricultural management under climate change scenarios. Both in regions that are already threatened and in those that until recently were not affected, it is necessary to study adaptation measures to avoid aggravating the impact of agricultural drought on crops, which could improve water productivity and future food security. 2023-11-07T00:00:00Z http://hdl.handle.net/10366/160282 [EN]In recent decades, there has been increasing interest in studying the variability in soil water properties and, specifically, the spatiotemporal variability in the soil water content. This is motivated by the notable theoretical and applied research interests in soil moisture dynamics and their implications for many natural processes. This study aimed to study whether there are variations in the spatial pattern of the temporal stability of soil moisture over time and to analyze the possible influences of certain hydroclimatic (soil water content, precipitation, and evapotranspiration) and soil factors (texture, bulk density, and organic matter content) on these variations. This study was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS, Spain) under Mediterranean conditions, with daily surface moisture data (0–5 cm depth) obtained from 20 stations for the 2006-2023 period. The results showed differences between the average pattern obtained with the 18-year data series and that obtained with the data series for each year. In more than half of the years studied, the representative station differed from that derived from the average pattern. The mean annual precipitation and summer precipitation characteristics seem to be the main factors influencing the variability in the spatial pattern of the temporal stability of soil moisture 2024-06-10T00:00:00Z http://hdl.handle.net/10366/160281 [EN]This study analyzes the temporal patterns of rainfed cereal phenology extracted from the GIMMS NDVI3g dataset in the main cereal-growing regions under a Mediterranean climate in Spain, Portugal, France and Italy during the period 1982–2022. The series before and after the beginning of the 21st century were analyzed separately. Phenological parameters were extracted using the modified dynamic threshold method, and their trends were analyzed. Correlation analyses were performed to study the relationships among these parameters and to analyze the influence of hydroclimatic variables on the start (SOS) and end (EOS) of the growing season. Results showed a temporal reversal in phenological trends between both study periods, coinciding with the Glob. warming hiatus. In the first period (1982–2002), SOS and EOS advanced (􀀀7.5 and 􀀀3.1 days, respectively), and the length of growing season (LOS) increased. However, during the second stage (2003–2022), SOS and EOS were delayed (7.5 and 1.7 days, respectively), and LOS decreased. Similar dynamics were observed for the influence of the hydroclimatic variables on SOS and EOS, stronger in the first period and weaker in the second. This study provides valuable information on the phenological dynamics of rainfed cereals that may be useful for their management and planning in climate change scenarios. 2024-05-24T00:00:00Z