•We examined the evolution of several drought metrics during the 2012 flash drought.•The Evaporative Stress Index (ESI) provided early warning of drought development.•NLDAS models sometimes depicted ...very different soil moisture anomalies.•Crop yield departures consistent with drought severity depicted by the ESI.
This study examines the evolution of several model-based and satellite-derived drought metrics sensitive to soil moisture and vegetation conditions during the extreme flash drought event that impacted major agricultural areas across the central U.S. during 2012. Standardized anomalies from the remote sensing based Evaporative Stress Index (ESI) and Vegetation Drought Response Index (VegDRI) and soil moisture anomalies from the North American Land Data Assimilation System (NLDAS) are compared to the United States Drought Monitor (USDM), surface meteorological conditions, and crop and soil moisture data compiled by the National Agricultural Statistics Service (NASS).
Overall, the results show that rapid decreases in the ESI and NLDAS anomalies often preceded drought intensification in the USDM by up to 6wk depending on the region. Decreases in the ESI tended to occur up to several weeks before deteriorations were observed in the crop condition datasets. The NLDAS soil moisture anomalies were similar to those depicted in the NASS soil moisture datasets; however, some differences were noted in how each model responded to the changing drought conditions. The VegDRI anomalies tracked the evolution of the USDM drought depiction in regions with slow drought development, but lagged the USDM and other drought indicators when conditions were changing rapidly. Comparison to the crop condition datasets revealed that soybean conditions were most similar to ESI anomalies computed over short time periods (2–4wk), whereas corn conditions were more closely related to longer-range (8–12wk) ESI anomalies. Crop yield departures were consistent with the drought severity depicted by the ESI and to a lesser extent by the NLDAS and VegDRI datasets.
We developed a global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer ...using circa- 2000 and 2005 Landsat images, incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas. Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs (RMSE =8.6% in 2000 and 11.9% in 2005), but showed improved accuracy in agricultural areas and increased discrimination of small forest patches. Against lidar measurements, the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF (RMSE=16.8% for MODIS-based vs. 17.4% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region. The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80% tree cover but showed greater potential for removal of errors through calibration to lidar, with post-calibration RMSE of 9.4% compared to 13.5% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover.org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multi-temporal depiction of Earth's tree cover available to the Earth science community.
Nos últimos anos, seguindo uma tendência europeia com várias décadas, a historiografia portuguesa tem construído séries estatísticas de longa duração para diversos indicadores económicos e sociais. ...Ainda que a agricultura seja uma das atividades historicamente mais relevantes, esses indicadores continuam a faltar. Resultando de uma longa pesquisa que tem tentado colmatar algumas das lacunas identificadas, apresentam-se as primeiras séries de produção agrícola regional para Portugal Continental. A análise começa no século XIX, quando os serviços oficiais passaram a recolher sistematicamente alguns dados estatísticos. O artigo está focado no processo de produção destas fontes históricas, avaliando como as mudanças no enquadramento institucional e nos objetivos do Estado podem ter influenciado a qualidade dos dados existentes para cada região. Seguidamente, traça um roteiro dos fundos documentais guardados e disponíveis nos arquivos nacionais e distritais, facilitando o desenvolvimento de futuras investigações.
Accurate and reliable information on the spatial distribution of major crops is needed for detecting possible production deficits with the aim of preventing food security crises and anticipating ...response planning. In this paper, we compared some of the most widely used global land cover datasets to examine their comparative advantages for cropland monitoring. Cropland class areas are compared for the following datasets: FAO-GLCshare (FAO Global Land Cover Network), Geowiki IIASA-Hybrid (Hybrid global land cover map from the International Institute of Applied System Analysis), GLC2000 (Global Land Cover 2000), GLCNMO2008 (Global Land Cover by National Mapping Organizations), GlobCover, Globeland30, LC-CCI (Land Cover Climate Change Initiative) 2010 and 2015, and MODISLC (MODIS Land Cover product). The methodology involves: (1) highlighting discrepancies in the extent and spatial distribution of cropland, (2) comparing the areas with FAO agricultural statistics at the country level, and (3) providing accuracy assessment through freely available reference datasets. Recommendations for crop monitoring at the country level are based on a priority ranking derived from the results obtained from analyses 2 and 3. Our results revealed that cropland information varies substantially among the analyzed land cover datasets. FAO-GLCshare and Globeland30 generally provided adequate results to monitor cropland areas, whereas LC-CCI2010 and GLC2000 are less unsuitable due to large overestimations in the former and out of date information and low accuracy in the latter. The recently launched LC-CCI datasets (i.e., LC-CCI2015) show a higher potential for cropland monitoring uses than the previous version (i.e., LC-CCI2010).
The purpose of the study is to reveal the peculiarities of the provincial and county zemstvos’ scientific and publishing activities in the field of agricultural statistics in Ukrainian terrains in ...the 1870s-1918. The methodology of work is based on the usage of general scientific (typology, classification), interdisciplinary, historical research methods and general methods of scientific knowledge (analysis and synthesis, generalization). Scientific novelty. For the first time, the publishing activity of provincial and county zemstvos in the field of agricultural statistics on Ukrainian lands was investigated. The analysis of publications published by zemstvo authorities in this field reveals the experience of possible approaches in building activities for modern self-government bodies. Conclusions. The analysis of the peculiarities of the scientific and publishing activities of provincial and county zemstvos in the field of agricultural statistics in Ukrainian lands in the 70s of the 19th century - 1918 made it possible to establish the following: provincial and district zemstvos actively disseminated the results of local statistical works and researches and made them as accessible as possible to a wide range of users. This is also confirmed by the obtained distribution by categories of printed publications. The revealed categories show that zemstvos used all possible ways to familiarize potential audience with the plans, publicize and explain the results of the statistical research of local agriculture.
The High Plains, Mississippi Embayment, and Central Valley aquifer systems within the United States are currently being overexploited for irrigation water supplies. The unsustainable use of ...groundwater resources in all three aquifer systems intensified from 2000 to 2008, making it imperative that we understand the consumptive processes and forces of demand that are driving their depletion. To this end, we quantify and track agricultural virtual groundwater transfers from these overexploited aquifer systems to their final destination. Specifically, we determine which US metropolitan areas, US states, and international export destinations are currently the largest consumers of these critical aquifers. We draw upon US government data on agricultural production, irrigation, and domestic food flows, as well as modeled estimates of agricultural virtual water contents to quantify domestic transfers. Additionally, we use US port-level trade data to trace international exports from these aquifers. In 2007, virtual groundwater transfers from the High Plains, Mississippi Embayment, and Central Valley aquifer systems totaled 17.93 km ³, 9.18 km ³, and 6.81 km ³, respectively, which is comparable to the capacity of Lake Mead (35.7 km ³), the largest surface reservoir in the United States. The vast majority (91%) of virtual groundwater transfers remains within the United States. Importantly, the cereals produced by these overexploited aquifers are critical to US food security (contributing 18.5% to domestic cereal supply). Notably, Japan relies upon cereals produced by these overexploited aquifers for 9.2% of its domestic cereal supply. These results highlight the need to understand the teleconnections between distant food demands and local agricultural water use.
Detailed and accurate statistics on crop productivity are key to inform decision-making related to sustainable food production and supply ensuring global food security. However, annual and ...high-resolution crop yield data provided by official agricultural statistics are generally lacking. Earth observation (EO) imagery, geodata on meteorological and soil conditions, as well as advances in machine learning (ML) provide huge opportunities for model-based crop yield estimation in terms of covering large spatial scales with unprecedented granularity. This study proposes a novel yield estimation approach that is bottom-up scalable from parcel to administrative levels by leveraging ML-ensembles, comprising of six regression estimators (base estimators), and multi-source geodata, including EO imagery. To ensure the approach’s robustness, two ensemble learning techniques are investigated, namely meta-learning through model stacking and majority voting. ML-ensembles were evaluated multi-annually and crop-specifically for three major winter crops, namely winter wheat (WW), winter barley (WB), and winter rapeseed (WR) in two German federal states, covering 140,000 to 155,000 parcels per year. ML-ensembles were evaluated at the parcel and district level for two German federal states against official yield reports, ranging from 2019 to 2022, based on metrics such as coefficient of determination (Formula: see text) and normalized root mean square error (Formula: see text). Overall, the most robustly performing ensemble learning technique was majority voting yielding Formula: see text and Formula: see text values of 0.74, 13.4% for WW, 0.68, 16.9% for WB, and 0.66, 14.1% for WR, respectively, through cross-validation at parcel level. At the district level, majority voting reached Formula: see text and Formula: see text ranges of 0.79–0.89, 7.2–8.1% for WW, 0.80–0.84, 6.0–9.9% for WB, and 0.60–0.78, 6.1–10.4% for WR, respectively. Capitalizing on ensemble learning-based majority voting, examples of unprecedented high-resolution crop yield maps at Formula: see text spatial resolution are presented. Implementing a scalable yield estimation approach, as proposed in this study, into crop yield reporting frameworks of public authorities mandated to provide official agricultural statistics would increase the spatial resolution of annually reported yields, eventually covering the entire cropland available. Such unprecedented data products delivered through map services may improve decision-making support for a variety of stakeholders across different spatial scales, ranging from parcel to higher administrative levels.
Advanced information on crop yield is important for crop management and food policy making. A data assimilation approach was developed to integrate remotely sensed data with a crop growth model for ...crop yield estimation. The objective was to model the crop yield when the input data for the crop growth model are inadequate, and to make the yield forecast in the middle of the growing season. The Cropping System Model (CSM)–Crop Environment Resource Synthesis (CERES)–Maize and the Markov Chain canopy Reflectance Model (MCRM) were coupled in the data assimilation process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and vegetation index products were assimilated into the coupled model to estimate corn yield in Indiana, USA. Five different assimilation schemes were tested to study the effect of using different control variables: independent usage of LAI, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), and synergic usage of LAI and EVI or NDVI. Parameters of the CSM–CERES–Maize model were initiated with the remotely sensed data to estimate corn yield for each county of Indiana. Our results showed that the estimated corn yield agreed very well with the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) data. Among different scenarios, the best results were obtained when both MODIS vegetation index and LAI products were assimilated and the relative deviations from the NASS data were less than 3.5%. Including only LAI in the model performed moderately well with a relative difference of 8.6%. The results from using only EVI or NDVI were unacceptable, as the deviations were as high as 21% and −13% for the EVI and NDVI schemes, respectively. Our study showed that corn yield at harvest could be successfully predicted using only a partial year of remotely sensed data.
•We simulate crop-water relations for maize on a global scale.•We compare uncertainties derived from different PET methods on the relations.•Uncertainties are significant, especially for crop water ...use and productivity.•Water availability plays an important role in the uncertainties.•A proper choice of PET methods is quite important for crop growth simulation.
Crop models are commonly used to investigate crop-water relations over different spatial scales. Estimating potential evapotranspiration (PET) is a basis for this investigation. Most crop models have built-in PET estimation methods. Using different methods can lead to very different PET estimates; but little is known about the sensitivity of large-scale crop model predictions on the choice of the PET estimation methods. In the work reported here, we used PEPIC, a grid-based EPIC (Environmental Policy Integrated Climate) model with a Python environment, to investigate the impacts of five different PET methods on estimated crop-water relations for maize on a global scale at a resolution of 30arcmin. Results show that the estimated PET varied largely among different PET methods for the same climate zones, leading to uncertainties in estimating crop-water relations. Uncertainties in water-related variables such as growing season evapotranspiration (GSET) and irrigation water requirement were more relevant than uncertainties in crop yields. Water availability played an important role in the uncertainties. All PET methods showed similar performance with respect to simulations of GSET for rainfed maize cultivation in low-rainfall regions, while there were large differences for regions with high rainfall. For irrigated agriculture, the estimated irrigation water requirement varied widely among the five PET methods, with a factor of 2 between the smallest and the largest estimates. Overall, using the Priestley-Taylor method led to lowest yield but highest GSET estimates. The Baier-Robertson and Hargreaves methods produced rather high GSET estimates for tropical and humid regions. The Penman-Monteith method gave the best yield estimates, compared to agricultural statistics. The results highlight the importance of considering the uncertainties resulting from the selection of PET estimation methods in investigating crop-water relations, particularly in predicting impacts of future climate change and in formulating appropriate water management strategies.
Agriculture in Brazilian Amazonia is going through a period of intensification. Crop mapping is important in understanding the way this intensification is occurring and the impact it is having. Two ...successive classifications based on MODIS (MODerate Resolution Imaging Spectroradiometer)-TERRA/EVI (Enhanced Vegetation Index) time series are applied (1) to map agricultural areas and (2) to identify five crop classes. These classes represent agricultural practices involving three commercial crops (soybean, maize and cotton) planted in single or double cropping systems. Both classifications are based on five steps: (1) analysis of the MODIS/EVI time series, (2) application of a smoothing algorithm, (3) application of a feature selection/extraction process to reduce the data set dimensionality, (4) application of a classifier and (5) application of a post-classification treatment. The first classification detected 95% of the agricultural areas (5 617 250 ha during the 2006–2007 harvest) and correlation coefficients with agricultural statistics exceeded 0.98 for the three crop classes at municipality level. The second classification (overall accuracy = 74% and kappa index = 0.675) allowed us to obtain the spatial variability mapping of agricultural practices in the state of Mato Grosso. A total of 30% of the total planted area was cultivated through double cropping systems, especially along the BR163 highway and in the Parecis plateau region.
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