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Economic statistics are frequently produced at an administrative level such as the subnational division. However, these measures may lack sufficient local variation for effective analysis of local economic development patterns and exposure to natural hazards. Agricultural gross domestic product (GDP) is a critical indicator for measurement of the primary sector, on which more than 2.5 billion people depend for their livelihoods, and it provides a key source of income for the entire household. Through a data-fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of agricultural GDP into a global gridded dataset at approximately 10×10 km for the year 2010 using satellite-derived indicators of the components that make up agricultural GDP, i.e., crop, livestock, fishery, hunting and forestry production. To illustrate the use of the new dataset, the paper estimates the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP, which amounts to around USD 432 billion of agricultural GDP circa 2010, with nearly 1.2 billion people living in those areas. The data are available on the World Bank Development Data Hub (10.57966/0j71-8d56;). [ABSTRACT FROM AUTHOR]

Economic statistics are frequently produced at an administrative level such as the sub-national division. However, these measures may lack sufficient local variation in the economic activities to analyze local economic development patterns and the exposure to natural hazards. Agriculture GDP is a critical indicator for measurement of the primary sector, on which more than 2.5 billion people depend on their livelihoods that provide a key source of income for the entire household (FAO, 2021). Through a data fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of Agricultural GDP into a global gridded dataset at approximately 10 x 10 kilometers using satellitederived indicators of the components that make up agricultural GDP, namely crop, livestock, fishery, hunting and timber production. The paper estimates the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP is an estimated US$432 billion of agricultural GDP circa 2010, where nearly 1.2 billion people live. The data are available on the World Bank Development Data Hub (DOI: http://doi.org/10.57966/0j71-8d56; IFPRI and World Bank, 2022). [ABSTRACT FROM AUTHOR]

Agricultural drought, which occurs due to a significant reduction in the moisture required for vegetation growth, is the most complex amongst all drought categories. The onset of agriculture drought is slow and can occur over vast areas with varying spatial effects, differing in areas with a particular vegetation land cover or specific agro-ecological sub-regions. These spatial variations imply that monitoring and forecasting agricultural drought require complex models that consider the spatial variations in a given region of interest. Hierarchical Bayesian models are suited for modelling such complex systems. Using partially pooled data with sub-groups that characterise spatial differences, these models can capture the sub-group variation while allowing flexibility and information sharing between these sub-groups. This paper's objective is to improve the accuracy and precision of agricultural drought forecasting in spatially diverse regions with a hierarchical Bayesian model. Results showed that the hierarchical Bayesian model was better at capturing the variability for the different agro-ecological zones and vegetation land covers compared to a regular Bayesian auto-regression distributed lags model. The forecasted vegetation condition and associated drought probabilities were more accurate and precise with the hierarchical Bayesian model at 4- to 10-week lead times. Forecasts from the hierarchical model exhibited higher hit rates with a low probability of false alarms for drought events in semi-arid and arid zones. The hierarchical Bayesian model also showed good transferable forecast skills over counties not included in the training data. [ABSTRACT FROM AUTHOR]

Better monitoring, reporting, and verification (MRV) of the amount, additionality, and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies, and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, modeling, and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called the Field Observatory (v1.0, https://www.fieldobservatory.org/ , last access: 3 February 2022). The Field Observatory is designed as an online service for near-real-time model–data synthesis, forecasting, and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site, which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices as well as provide an MRV tool for decision support. [ABSTRACT FROM AUTHOR]

In 2016, northern France experienced an unprecedented wheat crop loss. The cause of this event is not yet fully understood, and none of the most used crop forecast models were able to predict the event. However, this extreme event was likely due to a sequence of particular meteorological conditions, i.e. too few cold days in late autumn–winter and abnormally high precipitation during the spring season. Here we focus on a compound meteorological hazard (warm winter and wet spring) that could lead to a crop loss. This work is motivated by the question of whether the 2016 meteorological conditions were the most extreme possible conditions under current climate, and what the worst-case meteorological scenario would be with respect to warm winters followed by wet springs. To answer these questions, instead of relying on computationally intensive climate model simulations, we use an analogue-based importance sampling algorithm that was recently introduced into this field of research. This algorithm is a modification of a stochastic weather generator (SWG) that gives more weight to trajectories with more extreme meteorological conditions (here temperature and precipitation). This approach is inspired by importance sampling of complex systems. This data-driven technique constructs artificial weather events by combining daily observations in a dynamically realistic manner and in a relatively fast way. This paper explains how an SWG for extreme winter temperature and spring precipitation can be constructed in order to generate large samples of such extremes. We show that with some adjustments both types of weather events can be adequately simulated with SWGs, highlighting the wide applicability of the method. We find that the number of cold days in late autumn 2015 was close to the plausible minimum. However, our simulations of extreme spring precipitation show that considerably wetter springs than what was observed in 2016 are possible. Although the relation of crop loss in 2016 to climate variability is not yet fully understood, these results indicate that similar events with higher impacts could be possible in present-day climate conditions. [ABSTRACT FROM AUTHOR]

Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns; thus they are less informative for subsequent spatially explicit agricultural and environmental analyses. In the second part of the two-paper series, we introduce SPAM2010 – the latest global spatially explicit datasets on agricultural production circa 2010 – and elaborate on the improvement of the SPAM (Spatial Production Allocation Model) dataset family since 2000. SPAM2010 adds further methodological and data enhancements to the available crop downscaling modeling, which mainly include the update of base year, the extension of crop list, and the expansion of subnational administrative-unit coverage. Specifically, it not only applies the latest global synergy cropland layer (see Lu et al., submitted to the current journal) and other relevant data but also expands the estimates of crop area, yield, and production from 20 to 42 major crops under four farming systems across a global 5 arcmin grid. All the SPAM maps are freely available at the MapSPAM website (http://mapspam.info/ , last access: 11 December 2020), which not only acts as a tool for validating and improving the performance of the SPAM maps by collecting feedback from users but is also a platform providing archived global agricultural-production maps for better targeting the Sustainable Development Goals. In particular, SPAM2010 can be downloaded via an open-data repository (DOI: 10.7910/DVN/PRFF8V; IFPRI, 2019). [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays a significant role in many natural and anthropogenic systems which are essential to supporting life on Earth. Thus, accurate measurement and assessment of changes in soil moisture globally is of great value, including long-term historical assessment. Since the on-board cycle and detailed parameters of disparate sensors are different, the European Space Agency established the Climate Change Initiative (CCI) program to harmonize the available multisource SM data, producing long time-series surface SM datasets starting from 1978 to the present. However, the Soil Moisture Active Passive (SMAP) mission, launched in 2015, has shown more satisfactory performance in both spatial accuracy and in capturing pattern of temporal changes. In this paper, a random forest (RF) model was proposed to extend the superior SMAP dataset historically (named RF_SMAP), using the corresponding CCI data time-series. We assumed that the temporal changes in the SMAP dataset are similar generally to those in the available CCI dataset. Accordingly, the RF model was constructed using the temporal characteristics extracted from the CCI SM v05.2 data (coupled with three terrain characteristics and two location characteristics), which was migrated to the prediction of the RF_SMAP dataset. The available in-situ SM data and the real SMAP data from April 2015 to April 2016 were used as references to validate the predicted RF_SMAP data. It was shown that compared with the CCI dataset, the predicted RF_SMAP dataset is closer to the in-situ SM data and the real SMAP data. Moreover, the historical RF_SMAP dataset is more accurate than the widely used Global Land Evaporation Amsterdam Model (GLEAM) dataset in terms of average root mean square error (RMSE), bias (Bias), and Kling-Gutpa efficiency (KGE). Thus, the RF_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset, with an unbiased root mean square error (ubRMSE) of 0.035. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivum L.) is a major cereal crop of the world, which plays an important role in global food and nutritional security. In India, wheat grown areas are more as compared to other food crops, except for rice. The total area under wheat cultivation is 30.60 million hectares with production of 98.38 million tonnes and the productivity is 3.22 tonnes /hectare (DES, 2017). The main objective of this paper is to highlight the development of satellite-based methodology, compare the relative deviations (%) at national level, RMSE (%) and correlation coefficient at state level and correlation coefficient at district level between DES and FASAL estimates from 2013 to 2017. It was observed that the area and production estimates improved with improvement in the satellite resolution and ground truth data. During the last 10 years of estimation the spatial resolution of the satellite data has gradually improved from 23.5 meter of (Reourcesat-2, LISS-III) and finally 10 m of Sentinel-2, MSI, which is being currently used for acreage estimation purpose. Hooda R.S et al (2006) studied that the improvement in the spatial resolution, spectral and temporal resolution of the satellite data has also improved the crop discrimination. Both accuracy as well as precision of the estimates has improved over the years from 2013 to 2017, as reflected by relative deviation, RMSE (%) and Coefficient of correlation values at national, state and district level respectively. [ABSTRACT FROM AUTHOR]

Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model's applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP , last access: 4 April 2024). [ABSTRACT FROM AUTHOR]

Better monitoring, reporting and verification (MRV) of the amount, additionality and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost-efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near real-time field measurements, weather data, satellite imagery, modeling and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called Field Observatory (v1.0, fieldobservatory.org). Field Observatory is designed as an online service for near real-time model-data synthesis, forecasting and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices, and provide an MRV tool for decision-support. [ABSTRACT FROM AUTHOR]

In 2016, northern France experienced an unprecedented wheat crop loss. This extreme event was likely due to a sequence of particular meteorological conditions, i.e. too few cold days in late autumn-winter and an abnormally high precipitation during the spring season. The cause of this event is not fully understood yet and none of the most used crop forecast models were able to predict the event (Ben-Ari et al., 2018). Here we focus on a compound meteorological hazard (warm winter and wet spring) that could lead to a crop loss. This work is motivated by two main questions: were the 2016 meteorological conditions the most extreme under current climate? and what would be the worst case meteorological scenario that would lead to the worst crop loss? To answer these questions, instead of relying on computationally intensive climate model simulations, we use an analogue-based importance sampling algorithm that was recently introduced into this field of research (Yiou and Jézéquel, 2020). This algorithm is a modification of a stochastic weather generator (SWG) that gives more weight to trajectories with more extreme meteorological conditions (here temperature and precipitation). This approach is inspired from importance sampling of complex systems (Ragone et al., 2017). This data-driven technique constructs artificial weather events by combining daily observations in a dynamically realistic manner and in a relatively fast way. This paper explains how a SWG for extreme winter temperature and spring precipitation can be constructed in order to generate large samples of such extremes. We show that, with some adjustments, both types of weather events can be adequately simulated with SWGs, highlighting the wide applicability of the method. We find that the number of cold days in late autumn 2015 was close to the plausible maximum. But our simulations of extreme spring precipitation show that considerably wetter springs than what was observed in 2016 are possible. Although the crop loss of 2016 relation to climate variability is not fully understood yet, these results indicate that similar events with higher 20 impacts could be possible in present-day climate conditions. [ABSTRACT FROM AUTHOR]

Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns thus is less informative for subsequent spatially explicit agricultural and environmental analyses. In the second part of the two-paper series, we introduce SPAM2010 - the latest global spatially explicit datasets on agricultural production circa year 2010 - and elaborate on the improvement of the SPAM (Spatial Production Allocation Model) dataset family since year 2000. SPAM2010 adds further methodological and data enhancements to the available crop downscaling modeling: it not only applies the latest global synergy cropland layer (see Lu et al., submitted to the current journal) and other relevant data, but also expands the estimates of crop area, yield and production from 20 to 42 major crops under four farming systems across a global 5 arc-minute grid. All the SPAM maps are freely available at the MapSPAM website (http://mapspam.info/), which not only acts as a tool for validating and improving the performance of the SPAM maps by collecting feedbacks from users, but also dedicates as platform providing archived global agricultural production maps for better targeting the Sustainable Development Goals by making proper agricultural and rural development policies and investments. In particular, SPAM2010 can be downloaded via an open-data repository (DOI: https://doi.org/10.7910/DVN/PRFF8V, IFPRI, 2019). [ABSTRACT FROM AUTHOR]

In this study, we investigate applying deep learning (DL) models on a regional climate simulation produced by the Terrestrial Systems Modelling Platform (TSMP Ground to Atmosphere G2A) for vegetation health modeling and agricultural drought assessment. The TSMP simulation is performed in a free mode and the DL model is used in an intermediate step to synthesize Normalized Difference Vegetation Index (NDVI) and Brightness Temperature (BT) images from the TSMP simulation over Europe. These predicted images are then used to derive different vegetation and drought indices like NDVI anomaly, BT anomaly, Vegetation Condition Index (VCI), Thermal Condition Index (TCI), and Vegetation Health Index (VHI). To ensure reliability and to assess the model applicability with different seasonality and spatial variability, we provide an analysis of model biases and uncertainties across different regions over the Pan-Europe domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation on the long-term TSMP using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K° with coefficient of determination (R2) scores of 0.88 and 0.92 for NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. Our study could be used as a complimentary evaluation framework for climate change simulations with TSMP. Moreover, the developed DL model could be integrated with data assimilation and used for down-stream tasks, i.e., modelling the impact of extreme events on vegetation responses with different climate change scenarios. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP). [ABSTRACT FROM AUTHOR]

Today, the most popular approaches in agricultural forecasting leverage process-based crop models, crop monitoring data, and/or remote sensing imagery. Individually, each of these tools has its own unique advantages but is, nonetheless, limited in prediction accuracy, precision, or both. In this study we integrate in situ and remote sensing (RS) soil moisture observations with APSIM model through sequential data assimilation to evaluate the improvement in model predictions of downstream state variables across five experimental sites in the US Midwest. Four RS data products and in situ observations spanning 19 site years were used through two data assimilation approaches, namely ensemble Kalman filter (EnKF) and generalized ensemble filter (GEF), to constrain model states at observed time steps and estimate joint background and observation error matrices. Then, the assimilation's impact on estimates of soil moisture, yield, normalized difference vegetation index (NDVI), tile drainage, and nitrate leaching was assessed across all site years. When assimilating in situ observations, the accuracy of soil moisture forecasts in the assimilation layers was improved by reducing RMSE by an average of 17 % for 10 cm and ∼28 % for 20 cm depth soil layer across all site years. These changes also led to improved simulation of soil moisture in deeper soil layers by an average of 12 %. Although crop yield was improved by an average of 23 %, the greatest improvement in yield accuracy was demonstrated in site years with higher water stress, where assimilation served to increase available soil water for crop uptake. Alternatively, estimates of annual tile drainage and nitrate leaching were not well constrained across the study sites. Trends in drainage constraint suggest the importance of including additional data constraint such as evapotranspiration. The assimilation of RS soil moisture showed a weaker constraint of downstream model state variables when compared to the assimilation of in situ soil moisture. The median reduction in soil moisture RMSE for observed soil layers was lower, on average, by a factor of 5. However, crop yield estimates were still improved overall with a median RMSE reduction of 17.2 %. Crop yield prediction was improved when assimilating both in situ and remote sensing soil moisture observations, and there is strong evidence that yield improvement was higher when under water-stressed conditions. Comparisons of system performance across different combinations of remote sensing data products indicated the importance of high temporal resolution and accurate observation uncertainty estimates when assimilating surface soil moisture observations. [ABSTRACT FROM AUTHOR]

In the coming decades, a changing climate, the loss of high-quality land, the slowing in the annual yield of cereals, and increasing fertilizer use indicate that better agricultural water management strategies are needed. In this study, we designed FarmCan, a novel, robust remote sensing and machine learning (ML) framework to forecast farms' needed daily crop water quantity or needed irrigation (NI). We used a diverse set of simulated and observed near-real-time (NRT) remote sensing data coupled with a random forest (RF) algorithm and inputs about farm-specific situations to predict the amount and timing of evapotranspiration (ET), potential ET (PET), soil moisture (SM), and root zone soil moisture (RZSM). Our case study of four farms in the Canadian Prairies Ecozone (CPE) shows that 8 d composite precipitation (P) has the highest correlation with changes (Δ) of RZSM and SM. In contrast, 8 d PET and 8 d ET do not offer a strong correlation with 8 d P. Using R2 , root mean square error (RMSE), and Kling–Gupta efficiency (KGE) indicators, our algorithm could reasonably calculate daily NI up to 14 d in advance. From 2015 to 2020, the R2 values between predicted and observed 8 d ET and 8 d PET were the highest (80 % and 54 %, respectively). The 8 d NI also had an average R2 of 68%. The KGE of the 8 d ET and 8 d PET in four study farms showed an average of 0.71 and 0.50, respectively, with an average KGE of 0.62. FarmCan can be used in any region of the world to help stakeholders make decisions during prolonged periods of drought or waterlogged conditions, schedule cropping and fertilization, and address local government policy concerns. [ABSTRACT FROM AUTHOR]

GRACE (Gravity Recovery and Climate Experiment) and its follow-on mission have provided since 2002 monthly anomalies of total water storage (TWS), which are very relevant to assess the evolution of groundwater storage (GWS) at global and regional scales. However, the use of GRACE data for groundwater irrigation management is limited by their coarse (≃300 km) resolution. The last decade has thus seen numerous attempts to downscale GRACE data at higher – typically several tens of kilometres – resolution and to compare the downscaled GWS data with in situ measurements. Such comparison has been classically made in time, offering an estimate of the static performance of downscaling (classic validation). The point is that the performance of GWS downscaling methods may vary in time due to changes in the dominant hydrological processes through the seasons. To fill the gap, this study investigates the dynamic performance of GWS downscaling by developing a new metric for estimating the downscaling gain (new validation) against non-downscaled GWS. The new validation approach is tested over a 113 000 km 2 fractured granitic aquifer in southern India. GRACE TWS data are downscaled at 0.5 ∘ (≃50 km) resolution with a data-driven method based on random forest. The downscaling performance is evaluated by comparing the downscaled versus in situ GWS data over a total of 38 pixels at 0.5 ∘ resolution. The spatial mean of the temporal Pearson correlation coefficient (R) and the root mean square error (RMSE) are 0.79 and 7.9 cm respectively (classic validation). Confronting the downscaled results with the non-downscaling case indicates that the downscaling method allows a general improvement in terms of temporal agreement with in situ measurements (R=0.76 and RMSE = 8.2 cm for the non-downscaling case). However, the downscaling gain (new validation) is not static. The mean downscaling gain in R is about +30 % or larger from August to March, including both the wet and dry (irrigated) agricultural seasons, and falls to about +10 % from April to July during a transition period including the driest months (April–May) and the beginning of monsoon (June–July). The new validation approach hence offers for the first time a standardized and comprehensive framework to interpret spatially and temporally the quality and uncertainty of the downscaled GRACE-derived GWS products, supporting future efforts in GRACE downscaling methods in various hydrological contexts. [ABSTRACT FROM AUTHOR]

Agricultural drought mainly stems from reduced soil moisture and precipitation, and it causes adverse impacts on the growth of crops and vegetation, thereby affecting agricultural production and food security. In order to develop drought mitigation measures, reliable agricultural drought forecasting is essential. In this study, we developed an agricultural drought forecasting model based on canonical vine copulas in three dimensions (3C-vine model) in which antecedent meteorological drought and agricultural drought persistence were utilized as predictors. Furthermore, a meta-Gaussian (MG) model was selected as a reference to evaluate the forecast skill. The agricultural drought in China in August of 2018 was selected as a typical case study, and the spatial patterns of 1- to 3-month lead forecasts of agricultural drought utilizing the 3C-vine model resembled the corresponding observations, indicating the good predictive ability of the model. The performance metrics – the Nash–Sutcliffe efficiency (NSE), the coefficient of determination (R2), and the root-mean-square error (RMSE) – showed that the 3C-vine model outperformed the MG model with respect to forecasting agricultural drought in August for diverse lead times. Moreover, the 3C-vine model exhibited excellent forecast skill with respect to capturing the extreme agricultural drought over different selected typical regions. This study may help to guide drought early warning, drought mitigation, and water resource scheduling. [ABSTRACT FROM AUTHOR]

Predicting within-field crop yield early in the season can help address crop production challenges to improve farmers' economic return. While yield prediction with remote sensing has been a research aim for years, it is only recently that observations with the suited spatial and temporal resolutions have become accessible to improve crop yield predictions.Here we developed a yield prediction framework that integrates daily high-resolution (3 m) CubeSat imagery into the APSIM crop model. The approach trains a regression model that correlates simulated yield to simulated leaf area index (LAI) from APSIM. That relationship is then employed to determine the optimum date at which the regression best predicts yield from the LAI. Additionally, our approach can forecast crop yield by utilizing a particle filter to assimilate CubeSat-based LAI in the model APSIM to generate yield maps at 3 m several weeks before the optimum regression date. Our method was evaluated for a rainfed site located in the US Corn belt, using a collection of spatially varying yield data. The proposed approach does not need in situ data to rain the regression, with outcomes reporting that even with a single assimilation step, accurate yield predictions were provided up to 21 days before the optimum regression date. The spatial variability of crop yield was reproduced fairly well, with a good correlation against in situ measurements (R2 = 0.73 and RMSE = 1.69), demonstrating that high-resolution yield predictions early in the season have great potential to meet and improve upon digital agricultural goals. [ABSTRACT FROM AUTHOR]

Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ET o) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ET o forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ET o forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ET o forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ET o forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ET o forecasts. To test this hypothesis, we calibrate raw seasonal ET o forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ET o forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ET o forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ET o forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ET o forecasting will benefit from correcting time-dependent errors through trend reconstruction. [ABSTRACT FROM AUTHOR]

Reference crop evapotranspiration (ETo) is calculated using a standard formula with temperature, vapor pressure, solar radiation, and wind speed as input variables. ETo forecasts can be produced when forecasts of these input variables from numerical weather prediction (NWP) models are available. As raw ETo forecasts are often subject to systematic errors, statistical calibration is needed for improving forecast quality. The most straightforward and widely used approach is to directly calibrate raw ETo forecasts constructed with the raw forecasts of input variables. However, the predictable signal in ETo forecasts may not be fully implemented by this approach, which does not deal with error propagation from input variables to ETo forecasts. We hypothesize that correcting errors in input variables as a precursor to forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies that construct raw ETo forecasts with the raw (strategy i) or bias-corrected (strategy ii) input variables in ETo forecast calibration across Australia. Calibrated ETo forecasts based on bias-corrected input variables (strategy ii) demonstrate lower biases, higher correlation coefficients, and higher skills than forecasts produced by the calibration using raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could effectively reduce error propagation and enhance ETo forecast calibration. We anticipate that future NWP-based ETo forecasting will benefit from adopting the calibration strategy developed in this study to produce more skillful ETo forecasts. [ABSTRACT FROM AUTHOR]

This study examines methods of calibrating projections of future regional climate for the next 40–50 years using large single-model ensembles (the Community Earth System Model (CESM) Large Ensemble and Max Planck Institute (MPI) Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920–2016). The calibration methods were tested and verified using an "imperfect model" approach using the historical/representative concentration pathway 8.5 (RCP8.5) simulations from the Coupled Model Intercomparison Project 5 (CMIP5) archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely homogeneous Gaussian regression (HGR), is used for the subsequent analysis. As an extension to the HGR calibration method it is applied to dynamically decomposed data, in which the underlying data are separated into dynamical and residual components (HGR-decomp). Based on the verification results obtained using the imperfect model approach, the HGR-decomp method is found to produce more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes. [ABSTRACT FROM AUTHOR]

The region of southern Africa (SA) has a fragile food economy and is vulnerable to frequent droughts. Interventions to mitigate food insecurity impacts require early warning of droughts – preferably as early as possible before the harvest season (typically starting in April) and lean season (typically starting in November). Hydrologic monitoring and forecasting systems provide a unique opportunity to support early warning efforts, since they can provide regular updates on available root-zone soil moisture (RZSM), a critical variable for crop yield, and provide forecasts of RZSM by combining the estimates of antecedent soil moisture conditions with climate forecasts. For SA, this study documents the predictive capabilities of RZSM products from the recently developed NASA Hydrological Forecasting and Analysis System (NHyFAS). Results show that the NHyFAS products would have identified the regional severe drought event – which peaked during December–February of 2015–2016 – at least as early as 1 November 2015. Next, it is shown that during 1982–2016, February RZSM (Feb-RZSM) forecasts (monitoring product) available in early November (early March) have a correlation of 0.49 (0.79) with the detrended regional crop yield. It is also found that when the February RZSM forecast (monitoring product) available in early November (early March) is indicated to be in the lowest tercile, the detrended regional crop yield is below normal about two-thirds of the time (always), at least over the sample years considered. Additionally, it is shown that the February RZSM forecast (monitoring product) can provide "out-of-sample" crop yield forecasts with comparable (substantially better with 40 % reduction in mean error) skill to December–February ENSO. These results indicate that the NHyFAS products can effectively support food insecurity early warning in the SA region. Finally, since a framework similar to NHyFAS can be used to provide RZSM monitoring and forecasting products over other regions of the globe, this case study also demonstrates potential for supporting food insecurity early warning globally. [ABSTRACT FROM AUTHOR]

Seasonal climate forecast products offer useful information for farmers supporting them in planning and making decisions in their management practices, such as crop choice, planting and harvesting time, and water management. Driven by the need of stakeholders for tailored seasonal forecast products, our goal was to assess the applicability of seasonal forecast outputs in agriculture and to develop and pilot with stakeholders a set of seasonal climate outlooks for this sector in Finland. Finnish end users were involved in both the design and testing of the outlooks during the first pilot season of 2019. The seasonal climate outlooks were developed using the SEAS5 seasonal forecast system provided by ECMWF. To improve the prediction skill of the seasonal forecast data, several bias adjustment approaches were evaluated. The tested methods increased the quality of temperature forecast, but no suitable approach was found for eliminating the biases from precipitation data. Besides the widely applied indices, such as mean temperature, growing degree days, cold spell duration, total precipitation and dry conditions, new sector-oriented indices (such as progress of growing season) have been implemented and issued for various lead times (up to 3 months). The first result of forecast evaluation, the development of seasonal forecast indices and the first pilot season of May–October 2019 are presented. We found that the temperature-based outlooks performed well, with better performance skills for short lead times, providing useful information for the farmers in activity management. Precipitation indices had poor skills for each forecasted month, and further research is needed for improving the quality of forecast for Finland. The farmers who have tested the seasonal climate outlooks considered those beneficial and valuable, helping them in planning their activities. Following the first pilot season, further research and implementation work took place to improve our understanding of the skill of seasonal forecasts and increase the quality of tailored seasonal climate services. [ABSTRACT FROM AUTHOR]

Measurements of the surface–atmosphere exchange of ammonia (NH3) are necessary to study the emission and deposition processes of NH3 from managed and natural ecosystems. The eddy covariance technique, which is the most direct method for trace gas exchange measurements at the ecosystem level, requires trace gas detection at a fast sample frequency and high precision. In the past, the major limitation for measuring NH3 eddy covariance fluxes has been the slow time response of NH3 measurements due to NH3 adsorption on instrument surfaces. While high-frequency attenuation correction methods are used, large uncertainties in these corrections still exist, which are mainly due to the lack of understanding of the processes that govern the time response. We measured NH3 fluxes over a corn crop field using a quantum cascade laser spectrometer (QCL) that enables measurements of NH3 at a 10 Hz measurement frequency. The 5-month measurement period covered a large range of environmental conditions that included both periods of NH3 emission and deposition and allowed us to investigate the time response controlling parameters under field conditions. Without high-frequency loss correction, the median daytime NH3 flux was 8.59 ng m -2 s -1 during emission and - 19.87 ng m -2 s -1 during deposition periods, with a median daytime random flux error of 1.61 ng m -2 s -1. The overall median flux detection limit was 2.15 ng m -2 s -1 , leading to only 11.6 % of valid flux data below the detection limit. From the flux attenuation analysis, we determined a median flux loss of 17 % using the ogive method. No correlations of the flux loss with environmental or analyser parameters (such as humidity or inlet ageing) were found, which was attributed to the uncertainties in the ogive method. Therefore, we propose a new method that simulates the flux loss by using the analyser time response that is determined frequently over the course of the measurement campaign. A correction that uses as a function of the horizontal wind speed and the time response is formulated which accounts for surface ageing and contamination over the course of the experiment. Using this method, the median flux loss was calculated to be 46 %, which was substantially higher than with the ogive method. [ABSTRACT FROM AUTHOR]

Early yield assessment at local, regional and national scales is a major requirement for various users such as agriculture planners, policy makers, crop insurance companies and researchers. Current study explored a remote sensing-based approach of predicting the yield of Wheat, Kharif Rice and Rabi Rice at district level, using Vegetation Condition Index (VCI), under the FASAL programme. In order to make the estimates 14-years' historical database (2003–2016) of NDVI was used to derive the VCI. The yield estimation was carried out for 335 districts (136 districts of Wheat, 23 districts of Rabi Rice and 159 districts of Kharif Rice) for the period of 2016–17. NDVI products (MOD-13A2) of MODIS instrument on board Terra satellite at 16-day interval from first fortnight of peak growing period of crop were used to calculate the VCI. Stepwise regression technique was used to develop empirical models between VCI and historical yield of crops. Estimated yields are good in agreement with the actual district level yield with the R2 of, 0.78 for Wheat, 0.52 for Rabi Rice and 0.69 for Kharif Rice. For all the districts, the empirical models were found to be statistically significant. A large number of statistical parameters were computed to evaluate the performance of VCI-based models in predicting district-level crop yield. Though there was variation in model performance in different states and crops, overall, the study showed the usefulness of VCI, which can be used as an input for operational crop yield forecasting, at district level. [ABSTRACT FROM AUTHOR]

Sugarcane (Saccharum spp.) is a most important cash crop of India. India has the largest area under sugarcane cultivation in the world and is the world's second largest producer of sugarcane next to Brazil. Uttar Pradesh stands as the leading producer of sugarcane in India followed by Maharashtra. Under FASAL project, the crop acreage and yield forecasts at National/State/District level, is issued at two levels. This study was carried out to analyze under FASAL Project at Mahalanobis National Crop Forecast Centre, New Delhi during year 2018. Five-year state and district level area, yield and production estimates were observed and analyzed using different approaches in 6 major sugarcane producing states of India. The state and district-wise sugarcane area yield and production estimates of MNCFC were compared with the DES Data of the corresponding year. Two statistical parameters were computed namely, Root Mean Square Error RMSE (%) and correlation coefficient (R). The results when compared to the DES statistics revealed that the RMSE (%) for district level analysis were 23.4%, 30% and 18.35%, while the correlation coefficient (R) between the DES and FASAL estimates was computed 0.94, 0.95 and 0.60 for area, production and yield respectively. These results revealed that RS based technique can be effectively used for state level acreage, yield and production estimation. The national level statistics revealed that the correlation of FASAL sugarcane area and production is in good agreement with the DES estimates as compared to Yield estimates. [ABSTRACT FROM AUTHOR]

Rapeseed-mustard (Brassica spp.) is the major rabi oilseed crop of India. India is fourth largest contributor of oilseeds and Rapeseed-mustard contributing to around 11% of world's total production and about 28.6% in total oilseeds production of the country. More than 85% Rapeseed-mustard production comes from 5 States viz. Rajasthan [48%], Haryana [12%], MP [10%], UP [9%] and West Bengal [7%]. In the previous few years, remote sensing technique has been progressively more considered for evolving as an alternative, standardized, possibly cheaper and faster technology for crop acreage estimation. Furthermore, satellite remote sensing data have strong advantages in comparison with other monitoring techniques because it provides timely, synoptic and latest information of crop at various stages over large scales. Therefore, under FASAL project, cloud free crop season's images of different satellites (Sentinel-2, Resourcesat-2 and Landsat-8) were used and mustard crop was discriminated using Maximum Likelihood Classifier (MLC). Yield was estimated using different methods such as remote sensing derived NDVI, Agrometeorological yield model and Semi-Physical Model. The RMSE values for state level were found to be 4–17%, 8–19% and 13–23% for area, yield and production, respectively. The correlation coefficient (r) between DES and FASAL estimates were close to 0.9 in all the cases. The results of t-test at 5% level of significance inferred that FASAL and DES results were not significantly different. These results show that RS and weather-based techniques can be effectively used for pre-harvest acreage, yield and production estimation of mustard crop at district, state and national level. [ABSTRACT FROM AUTHOR]