Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction.

Large-scale continuous crop monitoring systems (CMS) are key to detect and manage agricultural production anomalies. Current CMS exploit meteorological and crop growth models, and satellite imagery, but have underutilized legacy sources of information such as operational crop expert surveys with long and uninterrupted records. We argue that crop expert assessments, despite their subjective and categorical nature, capture the complexities of assessing the "status" of a crop better than any model or remote sensing retrieval. This is because crop rating data naturally encapsulates the broad expert knowledge of many individual surveyors spread throughout the country, constituting a sophisticated network of "people as sensors" that provide consistent and accurate information on crop progress. We analyze data from the US Department of Agriculture (USDA) Crop Progress and Condition (CPC) survey between 1987 and 2019 for four major crops across the US, and show how to transform the original qualitative data into a continuous, probabilistic variable better suited to quantitative analysis. Although the CPC reflects the subjective perception of many surveyors at different locations, the underlying models that describe the reported crop status are statistically robust and maintain similar characteristics across different crops, exhibit long-term stability, and have nation-wide validity. We discuss the origin and interpretation of existing spatial and temporal biases in the survey data. Finally, we propose a quantitative Crop Condition Index based on the CPC survey and demonstrate how this index can be used to monitor crop status and provide earlier and more precise predictions of crop yields than official USDA forecasts released midseason.

Global drought trends and future projections.

Drought is one of the most difficult natural hazards to quantify and is divided into categories (meteorological, agricultural, ecological and hydrological), which makes assessing recent changes and future scenarios extremely difficult. This opinion piece includes a review of the recent scientific literature on the topic and analyses trends in meteorological droughts by using long-term precipitation records and different drought metrics to evaluate the role of global warming processes in trends of agricultural, hydrological and ecological drought severity over the last four decades, during which a sharp increase in atmospheric evaporative demand (AED) has been recorded. Meteorological droughts do not show any substantial changes at the global scale in at least the last 120 years, but an increase in the severity of agricultural and ecological droughts seems to emerge as a consequence of the increase in the severity of AED. Lastly, this study evaluates drought projections from earth system models and focuses on the most important aspects that need to be considered when evaluating drought processes in a changing climate, such as the use of different metrics and the uncertainty of modelling approaches. This article is part of the Royal Society Science+ meeting issue 'Drought risk in the Anthropocene'.

Genetic association with high-resolution climate data reveals selection footprints in the genomes of barley landraces across the Iberian Peninsula.

Landraces are local populations of crop plants adapted to a particular environment. Extant landraces are surviving genetic archives, keeping signatures of the selection processes experienced by them until settling in their current niches. This study intends to establish relationships between genetic diversity of barley (Hordeum vulgare L.) landraces collected in Spain and the climate of their collection sites. A high-resolution climatic data set (5 × 5 km spatial, 1-day temporal grid) was computed from over 2,000 temperature and 7,000 precipitation stations across peninsular Spain. This data set, spanning the period 1981-2010, was used to derive agroclimatic variables meaningful for cereal production at the collection sites of 135 barley landraces. Variables summarize temperature, precipitation, evapotranspiration, potential vernalization and frost probability at different times of the year and time scales (season and month). SNP genotyping of the landraces was carried out combining Illumina Infinium assays and genotyping-by-sequencing, yielding 9,920 biallelic markers (7,479 with position on the barley reference genome). The association of these SNPs with agroclimatic variables was analysed at two levels of genetic diversity, with and without taking into account population structure. The whole data sets and analysis pipelines are documented and available at https://eead-csic-compbio.github.io/barley-agroclimatic-association. We found differential adaptation of the germplasm groups identified to be dominated by reactions to cold temperature and late-season frost occurrence, as well as to water availability. Several significant associations pointing at specific adaptations to agroclimatic features related to temperature and water availability were observed, and candidate genes underlying some of the main regions are proposed.

Response of vegetation to drought time-scales across global land biomes.

We evaluated the response of the Earth land biomes to drought by correlating a drought index with three global indicators of vegetation activity and growth: vegetation indices from satellite imagery, tree-ring growth series, and Aboveground Net Primary Production (ANPP) records. Arid and humid biomes are both affected by drought, and we suggest that the persistence of the water deficit (i.e., the drought time-scale) could be playing a key role in determining the sensitivity of land biomes to drought. We found that arid biomes respond to drought at short time-scales; that is, there is a rapid vegetation reaction as soon as water deficits below normal conditions occur. This may be due to the fact that plant species of arid regions have mechanisms allowing them to rapidly adapt to changing water availability. Humid biomes also respond to drought at short time-scales, but in this case the physiological mechanisms likely differ from those operating in arid biomes, as plants usually have a poor adaptability to water shortage. On the contrary, semiarid and subhumid biomes respond to drought at long time-scales, probably because plants are able to withstand water deficits, but they lack the rapid response of arid biomes to drought. These results are consistent among three vegetation parameters analyzed and across different land biomes, showing that the response of vegetation to drought depends on characteristic drought time-scales for each biome. Understanding the dominant time-scales at which drought most influences vegetation might help assessing the resistance and resilience of vegetation and improving our knowledge of vegetation vulnerability to climate change.

Catalogue of drought events in peninsular Spanish along 1916-2020 period.

We leveraged the most extensive and detailed gridded database of monthly precipitation data across the Spanish mainland (MOPREDAScentury), encompassing 1916-2020 time period, to pinpoint the most severe drought events within this timeframe and analyse their spatio-temporal dynamics. To identify these events, we employed the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were identified as periods of at least three months where significantly dry conditions affected 20% or more of the study area, defined as grid cells with SPI values lower than -0.84. Our analysis revealed a total of 40 major drought events. Our catalogue contains detailed information on each episode's spatial extent, duration, severity, and spatio-temporal dynamics. The analysis of the propagation patterns of the events unveils substantial heterogeneity, implying that droughts stem from diverse atmospheric mechanisms, further influenced by complex local topography. The open-licensed drought database serves as a valuable resource. It not only facilitates exploration of drought onset and evolution mechanisms but also aids in assessing drought impact on agricultural and other socio-economic sectors.

Long-term effects of forest management on post-drought growth resilience: An analytical framework.

There is great interest in determining the effects of forest thinning as a tool to improve growth recovery from drought in different tree species and climatic conditions. However, we lack a robust framework to determine how transient are post-drought growth resilience and enhancement, and if such growth improvement involves an uncoupling with climate conditions. We used regression analysis to determine differences in growth, sensitivity to drought and previous-year growth, and long-term growth in five plantations of three pine species (Pinus halepensis Mill., Pinus nigra Arn. and Pinus sylvestris L.) under different thinning intensities. Then, we simulated post-drought and post-thinning growth trajectories based on fitted models, and we computed drought resistance, resilience and recovery indices based on these trajectories. Moreover, the simulation allowed us to calculate the time to recovery after a drought. Using this analytical framework, we found that thinning enhanced radial growth (between 85 and 150%, significant in all sites with p < 0.05), and reduced previous-year growth dependence (between -13 and -26%, significant in two out of five sites) and climatic dependence of growth (-23 to -49%, significant in two sites). We interpret these effects as a result of competition reduction by thinning and a transitory alleviation of growth climatic constraints. Thinning consistently improved drought resistance (+4 to +20%) and resilience (+1 to +4%). Recovery, on the contrary, was reduced (-1 to -15%). Since the growth loss during the drought was reduced due to higher drought resistance, the recovery was proportionally lower. Thinning reduced the time to recovery by one to two years. The thinning legacy effect persisted up to 15 to 20 years after thinning. Taken together, these findings enhance the benefits of adaptive silviculture in making pine plantations less vulnerable to unfavourable extreme climate events such as droughts. We present a novel and robust analytical framework to assess drought-thinning interactive effects on tree growth.

Monitoring Crop Status in the Continental United States Using the SMAP Level-4 Carbon Product.

Accurate monitoring of crop condition is critical to detect anomalies that may threaten the economic viability of agriculture and to understand how crops respond to climatic variability. Retrievals of soil moisture and vegetation information from satellite-based remote-sensing products offer an opportunity for continuous and affordable crop condition monitoring. This study compared weekly anomalies in accumulated gross primary production (GPP) from the SMAP Level-4 Carbon (L4C) product to anomalies calculated from a state-scale weekly crop condition index (CCI) and also to crop yield anomalies calculated from county-level yield data reported at the end of the season. We focused on barley, spring wheat, corn, and soybeans cultivated in the continental United States from 2000 to 2018. We found that consistencies between SMAP L4C GPP anomalies and both crop condition and yield anomalies increased as crops developed from the emergence stage (r: 0.4-0.7) and matured (r: 0.6-0.9) and that the agreement was better in drier regions (r: 0.4-0.9) than in wetter regions (r: -0.8-0.4). The L4C provides weekly GPP estimates at a 1-km scale, permitting the evaluation and tracking of anomalies in crop status at higher spatial detail than metrics based on the state-level CCI or county-level crop yields. We demonstrate that the L4C GPP product can be used operationally to monitor crop condition with the potential to become an important tool to inform decision-making and research.

Long-term thinning effects on tree growth, drought response and water use efficiency at two Aleppo pine plantations in Spain.

In Mediterranean areas where drought-induced forest dieback and tree mortality have been widely reported, it is still under debate how the likely risks of climate change will affect tree growth and consequently forest productivity. Increasing tree mortality has been associated not only to increased drought, but also to a lack of management in many dense pine forests and plantations, where warming may intensify tree-to-tree competition for soil water. This emphasizes the need of using silviculture to adapt dense stands of Mediterranean pine reforestations to warmer and drier conditions. Here we combined dendrochronology and C and O isotope analyses of wood in two Aleppo pine (Pinus halepensis) plantations, growing under semiarid conditions and experimentally thinned at high and moderate intensities along with control. The main aim was to understand the responses of radial growth and water use efficiency (WUEi) to different thinning intensities, and to analyze the effectiveness of thinning to enhance post-drought growth resilience. Thinning had a positive effect on growth, produced an increase of δ18O, reduced growth sensitivity to drought and decreased WUEi, suggesting a reduction of drought stress. These results were consistent across sites, and were significant even 20 years after the intervention took place. Considering the climate effects on growth through the SPEI drought index to calculate resistance and recovery indices, an increase of resistance after thinning was observed. We conclude that high thinning intensity (50% of basal area removed) is a useful silviculture intervention on Mediterranean Aleppo pine plantations that enhances their growth, and makes them less dependent on harsh climatic conditions, improving their resilience against drought and consequently making them better adapted to more unfavourable conditions.

Computation of rainfall erosivity from daily precipitation amounts.

Rainfall erosivity is an important parameter in many erosion models, and the EI30 defined by the Universal Soil Loss Equation is one of the best known erosivity indices. One issue with this and other erosivity indices is that they require continuous breakpoint, or high frequency time interval, precipitation data. These data are rare, in comparison to more common medium-frequency data, such as daily precipitation data commonly recorded by many national and regional weather services. Devising methods for computing estimates of rainfall erosivity from daily precipitation data that are comparable to those obtained by using high-frequency data is, therefore, highly desired. Here we present a method for producing such estimates, based on optimal regression tools such as the Gamma Generalised Linear Model and universal kriging. Unlike other methods, this approach produces unbiased and very close to observed EI30, especially when these are aggregated at the annual level. We illustrate the method with a case study comprising more than 1500 high-frequency precipitation records across Spain. Although the original records have a short span (the mean length is around 10 years), computation of spatially-distributed upscaling parameters offers the possibility to compute high-resolution climatologies of the EI30 index based on currently available, long-span, daily precipitation databases.

Mapping monthly rainfall erosivity in Europe.

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.

Reply to the comment on "Rainfall erosivity in Europe" by Auerswald et al.

Recently, in the Auerswald et al. (2015) comment on "Rainfall erosivity in Europe", 5 criticisms were addressed: i) the neglect of seasonal erosion indices, ii) the neglect of published studies and data, iii) the low temporal resolution of the data, especially of the maximum rain intensity, iv) the use of precipitation data instead of rain data and the subsequent deviation of the R-factor in Germany and Austria compared with previous studies, and v) the differences in considered time periods between countries. We reply as follows: (i) An evaluation of the seasonal erosion index at the European scale is, to our knowledge, not achievable at present with the available data but would be a future goal. Synchronous publication of the seasonal erosion index is not mandatory, specifically because seasonal soil loss ratios are not available at this scale to date. We are looking forward to the appropriate study by the authors of the comment, who assert that they have access to the required data. (ii) We discuss and evaluate relevant studies in our original work and in this reply; however, we cannot consider what is not available to the scientific community. (iii) The third point of critique was based on a misunderstanding by Auerswald et al. (2015), as we did indeed calculate the maximum intensity with the highest resolution of data available. (iv) The low R-factor values in Germany and the higher values in Austria compared with previous studies are not due to the involvement of snow but are rather due to a Pan-European interpolation. We argue that an interpolation across the borders of Austria creates a more reliable data set. (v) We agree that the use of a short time series or time series from different periods is generally a problem in all large-scale studies and requires improvement in the future. However, because this affects countries with a rather low variability of the R-factor in our study, we are confident that the overall results of the map are not biased. In conclusion, the Pan-European rainfall data compilation (REDES) was a great success and yielded data from 1541 stations with an average length of 17.1years and a temporal resolution of <60min. However, a Pan-European data collection will never be complete without the help and supply of data from its users. Thus, we invite the authors of the comment to share their data in the open REDES to help build even better rainfall-erosivity maps at regional or European scales.

Rainfall erosivity in Europe.

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 min. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 min using linear regression functions. Precipitation time series ranged from a minimum of 5 years to a maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression (GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha(-1) h(-1) yr(-1), with the highest values (>1000 MJ mm ha(-1) h(-1) yr(-1)) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha(-1) h(-1) yr(-1)) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also the highest in Mediterranean regions which implies high risk for erosive events and floods.

Assessing the effect of climate oscillations and land-use changes on streamflow in the central Spanish Pyrenees.

Plans to increase the amount of irrigated land in Mediterranean countries should consider how changes in climate and land-use affect water resources. In this study, both precipitation and temperature were used to analyze regional trends in discharge in the basins of the Central Spanish Pyrenees since the mid-20th century. Annual variations in the relationship between precipitation and discharge suggested that discharge was relatively lower in the second half of the study period, coinciding with major changes in land use. On a monthly scale, precipitation increased significantly in October, April, and July, and decreased in March, and temperature increased in January and February and decreased in April. Nevertheless, discharge has decreased significantly in most months in the past 50 years. Land-use and plant-cover changes are the only nonclimatic factor that can explain the loss of around 30% of the average annual discharge.

The management of a large Mediterranean reservoir: storage regimens of the Yesa Reservoir, Upper Aragon River Basin, Central Spanish Pyrenees.

Agriculture in Mediterranean countries is mainly based upon the irrigation of productive areas in the lowlands. For this reason, it is necessary to store large volumes of water in reservoirs located in mountain headwaters. These reservoirs have a relatively simple regimen of storage, increasing the water stored during the wet season (from October until May) and reaching the maximum volume shortly before the beginning of the hot, very dry season, when the water is released. This paper considers the storage regimen (inflow and outflow) of the Yesa Reservoir in the Spanish Pyrenees as an example of management of a large reservoir in a mountain Mediterranean environment, subject to a strong interannual variability. On average, the highest water storage level is achieved by retaining the high flows of the Aragón River in autumn and spring. Nevertheless, the irregularity of rainfalls and the existence of changes in the hydrological regimen lead to changes in the patterns of reservoir filling. Two patterns were identified in the Yesa Reservoir: (1) a quick increase of the stored volume in autumn, a stabilization in winter, and a new increase in spring; and (2) a continuous increase from October until May. These patterns are distributed in time over different periods since the construction of the reservoir in 1959, demonstrating the adjustment of the reservoir management to changes in the hydrological regimen.