Регрессионные модели прогнозирования урожайности озимой пшеницы в Украине

В статье решается задача оценки относительной эффективности использования спутниковых данных для прогнозирования урожайности озимой пшеницы в Украине на уровне отдельных областей. Для идентификации параметров моделей урожайности используются официальные статистические данные по урожайности озимой пшеницы на уровне областей за период 2000-2009 гг., валидация моделей выполняется на данных 2010 и 2011 года. Полученные результаты показали, что при настройке параметров моделей на данных 2000-2009 годов и 2000-2010 годов и независимом тестировании моделей на данных 2010 и 2011 годов соответственно, среднеквадратическая ошибка прогнозирования составляет примерно 6 ц/га.У статті вирішується задача оцінки відносної ефективності використання супутникових даних для прогнозування врожайності озимої пшениці в Україну на рівні окремих областей. Для ідентифікації параметрів моделей врожайності використовуються офіційні статистичні дані по врожайності озимої пшениці на рівні областей за період 2000-2009 рр., валідація моделей виконується на даних 2010 і 2011 року. Отримані результати показали, що при налаштуванні параметрів моделей на даних 2000-2009 років і 2000-2010 років і незалежному тестуванні моделей на даних 2010 і 2011 років відповідно, середньоквадратична помилка прогнозування становить приблизно 6 ц/га.In this paper we assess relative efficiency of using satellite data to winter wheat yield forecasting in Ukraine at oblast level, and compare efficiency of using regression and biophysical models to address this problem. For models identification we use official statistical data on winter wheat yield for 2000-2009, validation of models is done on independent data for 2010 and 2011. The achieved results showed that when training models for 2000-2009 and 2000-2010 years and validating for 2010 and 2011 respectively average root mean square error was approximately 0.6 t/ha

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

peer reviewedMathematical models have been widely employed for the simulation of growth dynamics of annual crops, thereby performing yield prediction, but not for fruit tree species such as jujube tree (Zizyphus jujuba). The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter. The model was established using data collected from dedicated field experiments performed in 2016–2018. Simulated growth dynamics of dry weights of leaves, stems, fruits, total biomass and leaf area index (LAI) agreed well with measured values, showing root mean square error (RMSE) values of 0.143, 0.333, 0.366, 0.624 t ha−1 and 0.19, and R2 values of 0.947, 0.976, 0.985, 0.986 and 0.95, respectively. Simulated phenological development stages for emergence, anthesis and maturity were 2, 3 and 3 days earlier than the observed values, respectively. In addition, in order to predict the yields of trees with different ages, the weight of new organs (initial buds and roots) in each growing season was introduced as the initial total dry weight (TDWI), which was calculated as averaged, fitted and optimized values of trees with the same age. The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI. The modelling performance was significantly improved when it considered TDWI integrated with tree age, showing good global (R2≥0.856, RMSE≤0.68 t ha−1) and local accuracies (mean R2≥0.43, RMSE≤0.70 t ha−1). Furthermore, the optimized TDWI exhibited the highest precision, with globally validated R2 of 0.891 and RMSE of 0.591 t ha−1, and local mean R2 of 0.57 and RMSE of 0.66 t ha−1, respectively. The proposed model was not only verified with the confidence to accurately predict yields of jujube, but it can also provide a fundamental strategy for simulating the growth of other fruit trees

UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques

Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using a VIs time series, and predicted yield using a peak descriptor derived from a VIs time series with 2.3 Mg DM ha−1 of the root mean square error (RMSE). The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production

Food industry site selection using geospatial technology approach

Food security has been an ongoing concern of governments and international organizations. One of the main issues in food security in Developing and Sanctioned Countries (DSCs) is establishment of food industries and related distributions in appropriate places. In this respect, geospatial technology offers the most up-to-date Land Cover (LC) information to improve site selection for assisting food security in the study area. Currently food security issues are not comprehensively addressed, especially in DSCs. In this research, ASTER L1B and LANDSAT satellite data were used to derive various LC biophysical parameters including build-up area, water body, forest, citrus, and rice fields in Qaemshahr city, Iran using different satellite-derived indices. A Product Level Fusion (PLF) approach was implemented to merge the outputs of the indices to prepare an improved LC map. The suitability of the proposed approach for LC mapping was evaluated in comparison with Support Vector Machine (SVM) and Artificial Neural Network (ANN) classification techniques. For implementing site selection, the outcomes of satellite-derived indices, as well as the city, village, road, railway, river, aqueduct, fault, casting, abattoir, cemetery, waste accumulation, wastewater treatment, educational centre, medical centre, military centre, asphalt factory, cement factory, and slope layers were obtained using Global Positioning System (GPS), on-screen digitizing, and image processing were used as input data. The Fuzzy Overlay and Weighted Linear Combination (WLC) methods were adopted to perform site selection process. The outcomes were then classified and analyzed based on the accessibility to main roads, cities and raw food materials. Finally, the existing industrial zones in the study area were evaluated for establishing food industries based on site selection results of this study. The results indicated higher performance of PLF method to provide up-to-date LC information with an overall accuracy and Kappa coefficient values of 95.95% and 0.95, respectively. The site selection result obtained using WLC method with the accuracy of 90% was superior, thus it was selected for further analyses. Based on the achieved results, the study has proven the applicability of current satellite data and geospatial technology for food industry site selection to resolve food security issues. In conclusion, site selection using geospatial technology provides a great potential for a reliable decision-making in food industry planning, as a significant issue in agro-based food security, especially in sanctioned countries

Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark

Multi-product characterization of surface soil moisture drydowns in the UK

The persistence or memory of soil moisture (θ) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in-situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple UK soil moisture products, including satellite-merged (i.e. TCM), in-situ (i.e. COSMOS-UK), hydrological model (i.e. G2G), statistical model (i.e. SMUK) and land surface model (LSM) (i.e. CHESS) data. The drydown decay time scale (τ) for all gridded products are computed at an unprecedented resolution of 1-2 km, a scale relevant to weather and climate models. While their range of τ differ (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyse the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted τ based on COSMOS and LSM are well correlated, with a bias of lower τ for COSMOS. Our findings contribute to a growing body of literature to characterize τ, with the aim of developing a method to systematically validate model soil moisture products at a range of scales

Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs

Distributed crop simulation models are typically confronted with considerable uncertainty in weather variables. In this paper the use of MeteoSat-derived meteorological products to replace weather variables interpolated from weather stations (temperature, reference evapotranspiration and radiation) is explored. Simulations for winter-wheat were carried for Spain, Poland and Belgium using both interpolated and MeteoSat-derived weather variables. The results were spatially aggregated to national and regional level and were evaluated by comparing the simulation results of both approaches and by assessing the relationships with crop yield statistics over the periods 1995¿2003 from EUROSTAT. The results indicate that potential crop yield can be simulated well using MeteoSat-derived meteorological variables, but that water-stress hardly occurs in the water-limited simulations. This is caused by a difference in reference evapotranspiration which was 20¿30% smaller in case of MeteoSat. As a result, the simulations using MeteoSat-derived meteorological variables performed considerably poorer in a regression analyses with crop yield statistics on national and regional level. Our results indicate that a recalibration of the model parameters is necessary before the MeteoSat-derived meteorological variables can be used operationally in the system

Crop growth modelling and crop yield forecasting using satellite derived meteorological inputs

One of the key challenges for operational crop monitoring and yield forecasting using crop models is to find spatially representative meteorological input data. Currently, weather inputs are often interpolated from low density networks of weather stations or derived from output from coarse (0.5 degree) numerical weather forecasting systems. The current study investigated the possibilities of deriving basic meteorological inputs (temperature, radiation, evapotranspiration) from observations of MeteoSat. A time-series of 10 years of decadal satellite products was used to run theWOFOST crop growth model and to simulate crop yield indicators for Spain, Belgium and Poland. Results with regard to the performance of the system using satellite derived meteorological inputs were compared with more traditional methods (interpolation from weather stations). Our results demonstrate that the MeteoSat derived temperature and radiation products can used be as input in a mechanistic crop model. Although the year to year variability in the potential crop simulations is not reproduced, it should be noted that the CGMS database itself contains large uncertainty in radiation input and therefore is not an absolute reference for comparison. For water-limited production levels we concluded that the MeteoSat based simulation are unable to reproduce the drought stress which usually occurs under Mediteranean conditions. This is a result of the fact that the MeteoSat based reference evapotranspiration is on average 30 percent smaller compared to the standard Penman reference evapotranspiration. The absence of drought stress in MeteoSat based simulations lead us to conclude that the EARS potential evapotranspiration product is not directly suitable for use in the WOFOST model. A considerable recalibration of the evapotranspiration related components of the WOFOST model will be necessary before we can use the MeteoSat based reference evapotranspiration in the model

Cereal yield modeling in Finland using optical and radar remote sensing : Special Issue Global Croplands

Peer reviewe