RICE YIELD ESTIMATION USING REMOTE SENSING AND CROP SIMULATION MODEL IN NALGONDA DISTRICT, TELANGANA

A study on “Rice yield estimation using Remote Sensing and crop simulation model in Nalgonda district, Telangana” was carried out during kharif, 2021. Precise and real-time agricultural yield data at the national, international and regional levels is becoming increasingly crucial for global food security. Crop yield forecasting could be very useful in advanced crop planning, strategy creation, and management. Because of the importance of yield prediction in food security, the present study used the APSIM-ORYZA model and remote sensing to estimate rice yield. The core objective of this study was to develop a method to integrate remotely sensed data and APSIM model for rice yield estimation in Nalgonda district, Telangana. This study includes mapping of rice growing areas and execution of APSIM model, followed by integration of remote sensing and crop simulation model for rice yield prediction and verification using government statistics. Based on stratification, two villages, Telakantigudem from Kangal mandal and Mallaram village from Kattangoor mandal in Nalgonda district were selected and ten fields from each village were chosen for the study to collect the measured LAI values with the help of ceptometer in the fields and the crop management data from the respected farmers. Crop classification was performed on Sentinel-1 and Sentinel-2 time series data using a Random Forest (RF) classifier and ground reference points collected from field surveys in the Google Earth Engine platform. The results demonstrated an overall accuracy of 92% and a kappa coefficient of 0.85, and rice area was validated with the crop coverage report (kharif, 2021) provided by the Department of Agriculture (DOA), Telangana state showed a relative variation of -0.16%. Remote sensing products like VV, VH AND VH/VV from Sentinel-1 and NIR, Red and NDVI from Sentinel-2 were derived using GEE and were calibrated with the measured LAI data collected from farmers’ fields. The result showed that there was a significant relation (R2=0.78) between NDVI and field LAI and hence it was considered for integration with the crop model output. Maps were derived showing spatial variation in crop extent, and leaf area index (LAI), which are crucial in yield assessment. Execution of APSIM-ORYZA model was done using the weather parameters, soil parameters, genetic coefficients and crop management data. The evaluation of the model with simulated yield and observed yield in the farmers’ fields showed linear regression of R2 = 0.79, root mean square error (RMSE)=804 kg ha-1 and mean absolute error (MAE)=728 kg ha-1. The overall spatially averaged model yield for the district showed 4925 kg ha-1 which is deviated by 2% from the average yield in the government statistics with 5024 kg ha-1. The study showed that by assimilation of remotely sensed data with the crop models, crop yields before harvest could be successfully predicted

Mapping and modeling groundnut growth and productivity in rainfed areas of Tamil Nadu

A research study was conducted at Tamil Nadu Agricultural University, Coimbatore during kharif and rabi 2015 to estimate groundnut area, model growth and productivity and assess the vulnerability of groundnut to drought using remote sensing techniques. Multi temporal Sentinel 1A satellite data at VV and VH polarization with 20 m spatial resolution was acquired from May, 2015 to January, 2016 at 12 days interval and processed using MAPscape-RICE software. Continuous monitoring was done for ground truth on crop parameters in twenty monitoring sites and validation exercise was done for accuracy assessment. Input files on soil, weather and management practices were generated and crop coefficients pertaining to varieties were developed to assess growth and productivity of groundnut using DSSAT CROPGRO-Peanut model. Outputs from remote sensing and DSSAT model were assimilated to generate LAI thereby groundnut yield spatially and validated against observed yields. Being a rainfed crop, vulnerability of groundnut to drought was assessed integrating different meteorological and spectral indices viz., Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI) and Water Requirement Satisfaction Index (WRSI).Spectral dB curve of groundnut was generated using temporal multi date Sentinel 1A data. A detailed analysis of temporal signatures of groundnut showed a minimum at sowing and a peak at pod development stage and decreasing thereafter towards maturity. Groundnut crop expressed a significant temporal behaviour and large dynamic range (-11.74 to -5.31 in VV polarization and -20.04 to -13.05 in VH polarization) during its growth period. Groundnut area map was generated using maximum likelihood classifier integrating multi temporal features with a classification accuracy of 87.2 per cent and a kappa score of 0.74. The total classified groundnut area in the study districts was 88023 ha covering 17817 and 22582 ha in Salem and Namakkal districts during kharif 2015 while Villupuram and Tiruvannamalai districts accounted for 22722 and 24903 ha respectively during rabi 2015. Blockwise statistics on groundnut area during both seasons were also generated. To model growth and productivity of groundnut in DSSAT, weather and soil input files were generated using weatherman and ‘S’ build respectively besides deriving genetic coefficients for CO 6, TMV 7 and VRI 2 varieties of groundnut. Growth and development variables of groundnut were simulated using CROPGROPeanut model i.e., days to emergence (7-9 days) and anthesis (25-32 days), canopy height (63 to 70 cm), maximum LAI (1.12 to 3.07) and biomass (4176 to 9576 kg ha-1 across twenty monitoring locations spatially. The resultant pod yield was simulated to be 1796 to 3060 kg ha-1 with a harvest index of 0.28 to 0.43. On comparison of LAI between observed (2.01 to 4.05) and simulated values (1.12 to 3.07) the CROPGRO-Peanut model was found to under estimate the values with R2, RMSE and NRMSE of 0.82, 1.10 and 34 per cent. However, the model predicted the biomass of groundnut with an agreement of 89 per cent through the simulated values of 4176 to9576 kg ha-1 as against the observed biomass to 4620 to 9959 kg ha-1. The simulated pod yields of groundnut in the study area were 1796 to 3060 kg ha-1 as compared to the observed yields of 2115 to 2750 kg ha-1. The overall agreement between simulated and observed yields was 84 per cent with the average errors of 0.81, 342 kg ha-1 and 16 percent for R2, RMSE and NRMSE respectively. LAI values of groundnut, generated spatially through suitable regression models using dB from satellite images and LAI from DSSAT, ranged from 1.31 to 3.23 with R2, RMSE and NRMSE of 0.86, 0.78 and 24 per cent respectively on comparison with observed values. Remote sensing based spatial estimation resulted in groundnut pod yields of 1570 to 3102 kg ha-1 across the study districts of Salem, Namakkal, Tiruvannamalai and Villupuram. In the 20 monitoring locations, the pod yields were estimated to be 1912 to 2975 kg ha-1 as against the observed pod yields of 1450 to 2750 kg ha-1 with a fairly good agreement of 80 per cent. The vulnerability of groundnut was assessed using different drought indices viz., SPI, NDVI and WRSI. Considering SPI, out of the total groundnut area of 88023 ha, an area of 86607 ha was found to be under near normal condition based on deviation of rainfall received during cropping season from historical precipitation. Similarly NDVI, an indicator of vegetation condition during the cropping season, showed that 14272 ha of groundnut area were under stressed condition during 2015. An area of 40981 ha in Villupuram and Tiruvannamalai districts was found to be under chances of crop failure based on Water Requirement Satisfaction index (WRSI). Major groundnut areas of Salem district (14188 ha) was under medium risk zone. Considering overall vulnerability, whole district of Villupuram was adjudged as highly vulnerable to drought with regard to groundnut cultivation whereas four blocks of Salem, eight blocks of Namakkal and all the blocks of Tiruvannamalai were found to be moderately vulnerable to drought

Combining remote sensing and crop modeling techniques to derive a nitrogen fertilizer application strategy

The crucial question in this thesis was how can remote sensing data and crop models be used to derive a N fertilizer strategy that is capable to lower the environmental side effects of N fertilizer application. This raised the following detailed objectives: The first objective (i) how N content determination via spectral reflectance is influenced by different leaves and positions on the leaf was investigated in Publication I. Different wheat plants were cultivated under different N levels and under drought stress in two hydroponic greenhouse trials. Spectral reflectance measurements were taken from three leaves and at three positions on the leaf for each plant. In total, 16 vegetation indices broadly used in the literature were calculated based on the spectral reflectance for each combination of leaf and position. The plant N content was determined by lab analyses. Neither the position on the leaf nor leaf number had an impact on the accuracy of plant N determination via spectral reflectance measurements. Therefore measurements taken at the canopy level seem to be a valid approach. However, if other stress symptoms like drought or disease infection occur, a differentiation between leaves and positions on the leaf might play a more crucial role. Publication II dealt with the second objective on (ii), how to incorporate leaf disease into the DSSAT wheat model to enable the simulation of the impact of leaf disease on yield. An integration of sensor information in crop growth models requires the update of model state variables. A model extension was developed by adding a pest damage module to the existing wheat model. The approach was tested on a two-year dataset from Argentina with different wheat cultivars and on a one-year dataset from Germany with different inoculum levels of septoria tritici blotch (STB). After the integration of disease infection, the accuracy of the simulated yield and leaf area index (LAI) was improved. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). A sensitivity analysis also showed a strong responsiveness of the model by the integration of different STB disease infection scenarios. Increasing the modeling accuracy even further a MM approach seems to be suitable. Assembling more models increases the complexity of the simulation and the involved calibration procedure especially if the user is not familiar with all models. To avoid these conflicts, Publication III evaluated the third objective (iii) if an automatic calibration procedure in a MM approach for winter wheat can eliminate the subjectivity factor in model calibration. The model calibration was performed on a 4-yr N wheat fertilizer trial in southwest Germany. The evaluation mean showed satisfying results for the calibration (d-Index 0.93) and evaluation dataset (d-Index 0.81). This lead to the fourth (iv) objective to use a MM approach to improve the overall modeling accuracy. The evaluation of a fertilizer trial showed an improved modeling accuracy in most cases, especially in the drought season 2018. Based on the combination of a MM approach and the incorporation of sensor data, a Nitrogen Application Prescription System (NAPS) was developed. The initial NAPS setup requires long term recorded data (yield, weather, and soil) to ensure proper MM calibration. After calibration, the current growing season conditions are required (weather, management information) until the N application date. Afterward, the NAPS incorporates remote sensing information and generated weather for running future N application scenarios. The selection of the proper amount of N is determined by economic and ecological criteria. Furthermore, in order to account for differences in in-field variabilities and to deliver a N prescription site-specifically, the NAPS concept has to be applied on a geospatial scale by adjusting soil parameters spatially. The NAPS concept has the potential to adjust the N application more economically and ecologically by using current sensor data, historical yield records, and future weather prediction to derive a more precise N application strategy. Finally, this concept exhibits the potential for reconciliation of the issue of an economic, agricultural production without harming the environment.In dieser Arbeit wurde eruiert, ob mit Hilfe von Sensordaten und Pflanzenwachstumsmodellen eine N-Düngemittelstrategie abgeleitet werden kann, die in der Lage ist die ökologischen Belastung zu verringern. Dies umfasste die Evaluation folgender Fragestellungen: (I) Wird die spektrale Reflexion und somit die Bestimmung der N-Konzentration durch die Messung an verschiedenen Blattetagen und -Positionen beeinflusst (Publikation I)? Für die Klärung dieser ersten Frage wurden in zwei hydroponischen Gewächshausversuchen Weizenpflanzen bei unterschiedlicher N-Exposition und Trockenstress kultiviert. Für jede Pflanze wurden spektrale Reflexionsmessungen an drei Blattetagen und an drei Positionen auf dem Blatt durchgeführt. Insgesamt wurden die 16 üblichsten auf spektraler Reflexion basierenden Vegetationsindizes für jede Kombination von Blattetage und -Position berechnet. Die N-Konzentration der Pflanze wurde durch Laboranalysen bestimmt. Weder die Position auf dem Blatt noch die Blattetage hatten einen Einfluss auf die Genauigkeit der Bestimmung der N-Konzentration der Pflanze durch spektrale Reflexionsmessungen. Daher sind Messungen auf Bestandsebene ausreichend. Falls jedoch weitere Stressfaktoren wie Trockenheit oder Krankheitsbefall auftreten, kann eine Differenzierung zwischen verschiedenen Blattetagen notwendig oder von Vorteil sein. In der nächsten Fragestellung (Publikation II) wurde untersucht, wie Blattkrankheiten in ein DSSAT-Weizenmodell integriert werden können, um so die Auswirkungen von Blattkrankheiten auf den Ertrag zu simulieren. Eine Modellerweiterung wurde entwickelt, durch die Integration eines Blattkrankheitsmoduls in das bestehende DSSAT Weizenmodell. Das Modul simuliert die Auswirkungen des täglichen Schadens durch die Krankheit auf die Photosynthese und den Blattflächenindex. Der Ansatz wurde an einem zweijährigen Datensatz aus Argentinien mit verschiedenen Weizensorten und an einem einjährigen Datensatz aus Deutschland mit verschiedenen Inokulumniveaus von Septoria tritici-Blotch (STB) getestet. Die Sensitivitätsanalyse zeigte die Möglichkeit des Modells, den Ertrag in einer exponentiellen Beziehung mit zunehmendem Infektionsgrad (0-70%) zu reduzieren. Das erweiterte Modell stellt somit eine Möglichkeit dar, STB-Infektionen standortspezifisch in Verbindung mit verfügbaren Sensordaten zu simulieren. Um die Modellierungsgenauigkeit noch weiter zu erhöhen, wurde der Einsatz eines MM-Ansatz geprüft. Die Kombination von verschiedenen Modellen erhöht die Komplexität der Simulation und des damit verbundenen Kalibrierungsverfahrens, insbesondere wenn der Benutzer nicht mit allen Modellen vertraut ist. Die dritte Fragestellung (iii) untersuchte daher, ob objektive Kalibrierungsergebnisse gewährleitet werden könnten, wenn die cultivar coefficients im Modell auf Basis tatsächlich gemessener Daten mittels eines neu entwickelten automatischen Calibrator-Programms optimiert wurden. Die Modellkalibrierung wurde an einem 4-jährigen-Weizendüngungsversuch in Südwestdeutschland durchgeführt. Die statistische Auswertung des Kalibrierverfahrens zeigte zufriedenstellende Ergebnisse und führte zur vierten Fragestellung. Die vierte Fragestellung befasste sich mit dem Thema, ob ein MM-Ansatz die Gesamtmodelliergenauigkeit verbessern kann. Die Auswertung des Düngemittelversuchs zeigte in den meisten Fällen eine verbesserte Modellierungsgenauigkeit, insbesondere in einem durch Wasserstress geprägten Versuchsjahr wie 2018. Unter Verwendung eines MM-Ansatzes, durch Anpassung der Modellvariablen und durch die Integration von Sensordaten wurde ein Nitrogen Application Prescription System (NAPS) entwickelt. Eine Voraussetzung für das NAPS-Konzepts ist das Vorhandensein von Langzeit-Daten (Ertrag, Klima- und Bodenbedingungen), um eine korrekte MM-Kalibrierung zu gewährleisten. Nach der Kalibrierung werden die Bedingungen der aktuellen Wachstumssaison (Wetter, Managementinformationen) bis zum Düngetermin benötigt. Anschließend berechnet das NAPS basierend auf Sensorinformationen und simulierten Wetterbedingungen verschiedene Düngeszenarien. Ökonomische und ökologische Kriterien bestimmen die optimierte Düngemenge. Darüber hinaus muss das NAPS-Konzept auf räumlicher Ebene arbeiten, indem es die Bodenparameter berücksichtigt. So kann unter Beachtung der Feldvariabilität eine standortspezifische N-Ausbringung gewährleistet werden. In Summe zeigte sich, dass NAPS die Düngung an ökonomische und ökologische Faktoren anpasst, indem es aktuelle Sensordaten, historische Ertragsaufzeichnungen und zukünftige Wettervorhersagen zur Ermittlung einer präziseren N-Ausbringung nutzt. Das Konzept hat so das Potenzial, die nachteiligen Auswirkungen einer Überdüngung zu begrenzen, so dass eine umweltfreundlichere Agrarproduktion gewährleistet wird

Combining remote sensing and crop modeling techniques to derive a nitrogen fertilizer application strategy

The crucial question in this thesis was how can remote sensing data and crop models be used to derive a N fertilizer strategy that is capable to lower the environmental side effects of N fertilizer application. This raised the following detailed objectives: The first objective (i) how N content determination via spectral reflectance is influenced by different leaves and positions on the leaf was investigated in Publication I. Different wheat plants were cultivated under different N levels and under drought stress in two hydroponic greenhouse trials. Spectral reflectance measurements were taken from three leaves and at three positions on the leaf for each plant. In total, 16 vegetation indices broadly used in the literature were calculated based on the spectral reflectance for each combination of leaf and position. The plant N content was determined by lab analyses. Neither the position on the leaf nor leaf number had an impact on the accuracy of plant N determination via spectral reflectance measurements. Therefore measurements taken at the canopy level seem to be a valid approach. However, if other stress symptoms like drought or disease infection occur, a differentiation between leaves and positions on the leaf might play a more crucial role. Publication II dealt with the second objective on (ii), how to incorporate leaf disease into the DSSAT wheat model to enable the simulation of the impact of leaf disease on yield. An integration of sensor information in crop growth models requires the update of model state variables. A model extension was developed by adding a pest damage module to the existing wheat model. The approach was tested on a two-year dataset from Argentina with different wheat cultivars and on a one-year dataset from Germany with different inoculum levels of septoria tritici blotch (STB). After the integration of disease infection, the accuracy of the simulated yield and leaf area index (LAI) was improved. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). A sensitivity analysis also showed a strong responsiveness of the model by the integration of different STB disease infection scenarios. Increasing the modeling accuracy even further a MM approach seems to be suitable. Assembling more models increases the complexity of the simulation and the involved calibration procedure especially if the user is not familiar with all models. To avoid these conflicts, Publication III evaluated the third objective (iii) if an automatic calibration procedure in a MM approach for winter wheat can eliminate the subjectivity factor in model calibration. The model calibration was performed on a 4-yr N wheat fertilizer trial in southwest Germany. The evaluation mean showed satisfying results for the calibration (d-Index 0.93) and evaluation dataset (d-Index 0.81). This lead to the fourth (iv) objective to use a MM approach to improve the overall modeling accuracy. The evaluation of a fertilizer trial showed an improved modeling accuracy in most cases, especially in the drought season 2018. Based on the combination of a MM approach and the incorporation of sensor data, a Nitrogen Application Prescription System (NAPS) was developed. The initial NAPS setup requires long term recorded data (yield, weather, and soil) to ensure proper MM calibration. After calibration, the current growing season conditions are required (weather, management information) until the N application date. Afterward, the NAPS incorporates remote sensing information and generated weather for running future N application scenarios. The selection of the proper amount of N is determined by economic and ecological criteria. Furthermore, in order to account for differences in in-field variabilities and to deliver a N prescription site-specifically, the NAPS concept has to be applied on a geospatial scale by adjusting soil parameters spatially. The NAPS concept has the potential to adjust the N application more economically and ecologically by using current sensor data, historical yield records, and future weather prediction to derive a more precise N application strategy. Finally, this concept exhibits the potential for reconciliation of the issue of an economic, agricultural production without harming the environment.In dieser Arbeit wurde eruiert, ob mit Hilfe von Sensordaten und Pflanzenwachstumsmodellen eine N-Düngemittelstrategie abgeleitet werden kann, die in der Lage ist die ökologischen Belastung zu verringern. Dies umfasste die Evaluation folgender Fragestellungen: (I) Wird die spektrale Reflexion und somit die Bestimmung der N-Konzentration durch die Messung an verschiedenen Blattetagen und -Positionen beeinflusst (Publikation I)? Für die Klärung dieser ersten Frage wurden in zwei hydroponischen Gewächshausversuchen Weizenpflanzen bei unterschiedlicher N-Exposition und Trockenstress kultiviert. Für jede Pflanze wurden spektrale Reflexionsmessungen an drei Blattetagen und an drei Positionen auf dem Blatt durchgeführt. Insgesamt wurden die 16 üblichsten auf spektraler Reflexion basierenden Vegetationsindizes für jede Kombination von Blattetage und -Position berechnet. Die N-Konzentration der Pflanze wurde durch Laboranalysen bestimmt. Weder die Position auf dem Blatt noch die Blattetage hatten einen Einfluss auf die Genauigkeit der Bestimmung der N-Konzentration der Pflanze durch spektrale Reflexionsmessungen. Daher sind Messungen auf Bestandsebene ausreichend. Falls jedoch weitere Stressfaktoren wie Trockenheit oder Krankheitsbefall auftreten, kann eine Differenzierung zwischen verschiedenen Blattetagen notwendig oder von Vorteil sein. In der nächsten Fragestellung (Publikation II) wurde untersucht, wie Blattkrankheiten in ein DSSAT-Weizenmodell integriert werden können, um so die Auswirkungen von Blattkrankheiten auf den Ertrag zu simulieren. Eine Modellerweiterung wurde entwickelt, durch die Integration eines Blattkrankheitsmoduls in das bestehende DSSAT Weizenmodell. Das Modul simuliert die Auswirkungen des täglichen Schadens durch die Krankheit auf die Photosynthese und den Blattflächenindex. Der Ansatz wurde an einem zweijährigen Datensatz aus Argentinien mit verschiedenen Weizensorten und an einem einjährigen Datensatz aus Deutschland mit verschiedenen Inokulumniveaus von Septoria tritici-Blotch (STB) getestet. Die Sensitivitätsanalyse zeigte die Möglichkeit des Modells, den Ertrag in einer exponentiellen Beziehung mit zunehmendem Infektionsgrad (0-70%) zu reduzieren. Das erweiterte Modell stellt somit eine Möglichkeit dar, STB-Infektionen standortspezifisch in Verbindung mit verfügbaren Sensordaten zu simulieren. Um die Modellierungsgenauigkeit noch weiter zu erhöhen, wurde der Einsatz eines MM-Ansatz geprüft. Die Kombination von verschiedenen Modellen erhöht die Komplexität der Simulation und des damit verbundenen Kalibrierungsverfahrens, insbesondere wenn der Benutzer nicht mit allen Modellen vertraut ist. Die dritte Fragestellung (iii) untersuchte daher, ob objektive Kalibrierungsergebnisse gewährleitet werden könnten, wenn die cultivar coefficients im Modell auf Basis tatsächlich gemessener Daten mittels eines neu entwickelten automatischen Calibrator-Programms optimiert wurden. Die Modellkalibrierung wurde an einem 4-jährigen-Weizendüngungsversuch in Südwestdeutschland durchgeführt. Die statistische Auswertung des Kalibrierverfahrens zeigte zufriedenstellende Ergebnisse und führte zur vierten Fragestellung. Die vierte Fragestellung befasste sich mit dem Thema, ob ein MM-Ansatz die Gesamtmodelliergenauigkeit verbessern kann. Die Auswertung des Düngemittelversuchs zeigte in den meisten Fällen eine verbesserte Modellierungsgenauigkeit, insbesondere in einem durch Wasserstress geprägten Versuchsjahr wie 2018. Unter Verwendung eines MM-Ansatzes, durch Anpassung der Modellvariablen und durch die Integration von Sensordaten wurde ein Nitrogen Application Prescription System (NAPS) entwickelt. Eine Voraussetzung für das NAPS-Konzepts ist das Vorhandensein von Langzeit-Daten (Ertrag, Klima- und Bodenbedingungen), um eine korrekte MM-Kalibrierung zu gewährleisten. Nach der Kalibrierung werden die Bedingungen der aktuellen Wachstumssaison (Wetter, Managementinformationen) bis zum Düngetermin benötigt. Anschließend berechnet das NAPS basierend auf Sensorinformationen und simulierten Wetterbedingungen verschiedene Düngeszenarien. Ökonomische und ökologische Kriterien bestimmen die optimierte Düngemenge. Darüber hinaus muss das NAPS-Konzept auf räumlicher Ebene arbeiten, indem es die Bodenparameter berücksichtigt. So kann unter Beachtung der Feldvariabilität eine standortspezifische N-Ausbringung gewährleistet werden. In Summe zeigte sich, dass NAPS die Düngung an ökonomische und ökologische Faktoren anpasst, indem es aktuelle Sensordaten, historische Ertragsaufzeichnungen und zukünftige Wettervorhersagen zur Ermittlung einer präziseren N-Ausbringung nutzt. Das Konzept hat so das Potenzial, die nachteiligen Auswirkungen einer Überdüngung zu begrenzen, so dass eine umweltfreundlichere Agrarproduktion gewährleistet wird

Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach

This study reviews soil water balance (SWB) model approaches to determine crop irrigation requirements and scheduling irrigation adopting the FAO56 method. The Kc-ETo approach is discussed with consideration of baseline concepts namely standard vs. actual Kc concepts, as well as single and dual Kc approaches. Requirements for accurate SWB and appropriate parameterization and calibration are introduced. The one-step vs. the two-step computational approaches is discussed before the review of the FAO56 method to compute and partition crop evapotranspiration and related soil water balance. A brief review on transient state models is also included. Baseline information is concluded with a discussion on yields prediction and performance indicators related to water productivity. The study is continued with an overview on models development and use after publication of FAO24, essentially single Kc models, followed by a review on models following FAO56, particularly adopting the dual Kc approach. Features of dual Kc modeling approaches are analyzed through a few applications of the SWB model SIMDualKc, mainly for derivation of basal and single Kc, extending the basal Kc approach to relay intercrop cultivation, assessing alternative planting dates, determining beneficial and nonbeneficial uses of water by an irrigated crop, and assessing the groundwater contribution to crop ET in the presence of a shallow water table. The review finally discusses the challenges placed to SWB modeling for real time irrigation scheduling, particularly the new modeling approaches for large scale multi-users application, use of cloud computing and adopting the internet of things (IoT), as well as an improved wireless association of modeling with soil and plant sensors. Further challenges refer to the use of remote sensing energy balance and vegetation indices to map Kc, ET and crop water and irrigation requirements. Trends are expected to change research issues relative to SWB modeling, with traditional models mainly used for research while new, fastresponding and multi-users models based on cloud and IoT technologies will develop into applications to the farm practice. Likely, the Kc-ETo will continue to be used, with ETo from gridded networks, re-analysis and other sources, and Kc data available in real time from large databases and remote sensinginfo:eu-repo/semantics/publishedVersio

Spatial crop-water variations in rainfed wheat systems: From simulation modelling to site-specific management

In sloping fields, rainfed crops experience different degrees of water stress caused by spatial variations in water and, consequently, yields also vary spatially within a field. This offers opportunities for precision agriculture through site-specific management. However, while significant advances have been accomplished in the engineering aspects of precision agriculture, such as increasing spatial resolution of data systems and automation, much less effort has been dedicated to the simulation of within field crop responses to spatial variations. Most studies on rainfed yield gaps ignore intra-plot variability, but if crop models are to be used in assisting site-specific management, they may greatly benefit from spatial water modelling approaches capable of accurately representing and simulating within-field variation of water-related processes. This doctoral thesis represents a novel contribution to the agronomy of rainfed agricultural systems, evaluating the role played by water flows in areas of undulating topography in determining the spatial variations of wheat yield. The thesis has been carried out in chapters that are associated by following an integrative approach. The thesis first reviewed some of the most widely adopted crop and hydrologic models and explored new opportunities for simulating spatial water variations at crop field level through the incorporation of lateral inflow at lower elevation zones of the field. From this standpoint, the spatial variations of yield gaps in rainfed wheat, caused by lateral flows from high to low areas, were assessed in Córdoba, Spain. From an agronomic perspective, water lateral inflows (LIF) due to surface and subsurface runoff contribute to yield variations in rainfed wheat production systems such as the one studied here. The net contribution of these flows to spatial variations of rainfed potential yields showed to be relevant but highly irregular among years. Despite the inter-annual variability, typical of Mediterranean conditions, the occurrence of LIF caused simulated wheat yields to vary +16% from up to downslope areas of the field. Average crop yield ranged from 1.3 to 5.4 Mg grain yield (GY) ha−1. The net yield responses to LIF, in downslope areas were on average 383 kg grain yield (GY) ha−1, and the LIF marginal water productivity reached 24.6 (±13.2) kg GY ha−1 mm−1 in years of maximum responsiveness. Such years of maximum responsiveness were associated with low rainfall during the vegetative stages of the crop in combination with LIF occurring at post-flowering stages. However, under field conditions, these differences were only visible in one of the two experimental years. The economic implications associated with multiple scenarios of variable application rate of nitrogen were explored through a case study and several recommendations were proposed. Both farm size (i.e., annual sown area) and topographic structure impacted the dynamics of investment returns. Under current policy-prices conditions, the adoption of variable application rate would have an economic advantage in farms similar to that of the case study with an annual sown area greater than 567 ha year−1. Nevertheless, current trends on energy prices, transportation costs and impacts on both cereal prices and fertilizers costs enhance the viability of variable application rate adoption for a wider population of farm types. The profitability of adopting VAR improves under such scenarios and, in the absence of additional policy support, the minimum area for adoption of variable application rate decreases to a farm size range of 68-177 ha year−1. The combination of price increases with the introduction of an additional subsidy on crop area could substantially lower the adoption threshold down to 46 ha year−1, turning this technology economically viable for a much wider population of farmers.En campos en pendiente, los cultivos de secano experimentan diferentes grados de estrés hídrico causados por variaciones espaciales de la humedad en el suelo, y los rendimientos varían espacialmente dentro del mismo campo. Esta variabilidad supone una oportunidad para la agricultura de precisión a través del manejo espacialmente variable. Sin embargo, si bien se han logrado avances significativos en los aspectos de la ingeniería de la variación espacial, como el aumento de la resolución espacial de los sistemas de datos y la automatización, se ha avanzado mucho menos en relación a la simulación de las respuestas de los cultivos a las variaciones espaciales de la humedad y los flujos hídricos. La mayoría de los estudios sobre las brechas de rendimiento de secano ignoran la variabilidad dentro de la parcela. Sin embargo, el uso de modelos de simulación de cultivos como medida de apoyo a los sistemas de gestión espacialmente variable, requiere que los enfoques de modelación espacial del agua sean capaces de representar y simular con precisión la variación dentro del campo de los factores relacionados con el agua disponible y la respuesta de los cultivos. Esta tesis doctoral representa una nueva contribución a la agronomía de los sistemas agrícolas de secano, con énfasis en el papel que juegan los flujos de agua en zonas de topografía ondulada en la determinación de las variaciones espaciales del rendimiento del trigo. La tesis se ha desarrollado en capítulos que se complementan siguiendo un enfoque integrador. La presente tesis doctoral revisó algunos de los modelos hidrológicos y de cultivo más ampliamente adoptados y exploró nuevas oportunidades para simular variaciones espaciales del agua a nivel de campo mediante la incorporación del flujo lateral de escorrentía superficial y sub-superficial en las zonas de menor elevación del campo. Desde este punto de vista, se evaluaron las variaciones espaciales de las brechas de rendimiento en trigo de secano, en Córdoba, España, que son causadas por flujos laterales de los puntos altos a los bajos. Desde una perspectiva agronómica, las entradas laterales del agua contribuyen a las variaciones de rendimiento en los sistemas de producción de trigo de secano como el que se ha estudiado en el ámbito de esta tesis. La contribución neta de estos flujos a las variaciones espaciales de los rendimientos potenciales de secano se mostró relevante pero altamente irregular entre diferentes años. A pesar de la variabilidad interanual, típica de las condiciones mediterráneas, la existencia de dichos flujos hizo que los rendimientos de trigo simulados variaran un +16% desde las áreas más elevadas de un campo hacia abajo. El rendimiento medio observado osciló entre 1.3 y 5.4 Mg de rendimiento de grano (GY) ha−1. Las respuestas de rendimiento neto al flujo lateral, cuenca abajo, fueron en promedio 383 kg de rendimiento de grano (GY) ha−1, y la productividad marginal de agua de LIF alcanzó 24.6 (±13.2) kg GY ha−1 mm−1 en años de máxima capacidad de respuesta. Dichos años de máxima capacidad de respuesta se asociaron con bajas precipitaciones durante las etapas vegetativas del cultivo en combinación con flujos laterales en las etapas posteriores a la floración. En condiciones de campo, estas diferencias solo fueron visibles en uno de los dos años experimentales. Las implicaciones económicas asociadas con múltiples escenarios de tasa de aplicación variable de nitrógeno se exploraron a través de un caso de estudio y se propusieron varias recomendaciones. Tanto el tamaño de la finca (el área sembrada anual) como la estructura topográfica afectaron la dinámica de los rendimientos de la inversión. Bajo las condiciones actuales de política agrícola, y de precios, la adopción de la tasa de aplicación variable tendría una ventaja económica en fincas similares a la del caso de estudio con un área sembrada anual superior a 567 ha año−1. Sin embargo, las tendencias actuales en los precios de la energía, los costes de transporte y los impactos tanto en los precios de los cereales como en los costes de los fertilizantes mejoran la viabilidad de la adopción de esta tecnología para una población más amplia de tipos de fincas. La rentabilidad de la adopción de aplicación variable de nitrógeno mejora bajo dichos escenarios y, en ausencia de apoyos adicionales, el área mínima para la adopción de aplicación variable disminuye hasta un rango de 68-177 ha año−1 de área de siembra. La combinación de aumentos de precios con la introducción de un subsidio adicional asociado al área de cultivo podría reducir sustancialmente el umbral de adopción hasta 46 ha año−1, lo que hace que la tecnología sea económicamente viable para una población mucho más amplia de agricultores

Improving Retrievals of Crop Vegetation Parameters from Remote Sensing Data

Agricultural systems are difficult to model because crop growth is driven by the strongly nonlinear interaction of Genotype x Environment x Management (G x E x M) factors. Due to the nonlinearity in the interaction of these factors, the amount of data necessary to develop and utilize models to accurately predict the performance of agricultural systems at an operational scale is large. Satellite remote sensing provides the potential to vastly increase the amount of data available for modelling agricultural systems as a result of its high revisit time and spatial coverage. Unfortunately, there have been significant difficulties in deploying remote sensing for many agricultural modelling applications because of the uncertainty involved in the retrievals. In this dissertation, we show that collecting farmer-provided agro-managment information has the potential to reduce the uncertainty in the retrieval products obtained from remote sensing observations. Specifically, both field-scale and regional-scale analysis are used to show that secondary factor variability is a very significant cause of uncertainty in both crop growth modelling and agricultural remote sensing that needs to be addressed through increased data collection. In order to address this need for increased data availability, a method is developed that allows geolocated crop growth model simulations to be used to train satellite-based crop state variable retrievals, which is then validated at regional scale. The method developed provides a general robust methodology to create a large-scale platform that would allow farmers to share data with government agencies and universities to improve crop state variable retrievals and crop growth modelling and provide farmers, government, industry, and researchers with insights and predictive capability into crop growth at both field and regional scales

Agrometeorological forecasting

Agrometeorological forecasting covers all aspects of forecasting in agrometeorology. Therefore, the scope of agrometeorological forecasting very largely coincides with the scope of agrometeorology itself. All on-farm and regional agrometeorological planning implies some form of impact forecasting, at least implicitly, so that decision-support tools and forecasting tools largely overlap. In the current chapter, the focus is on crops, but attention is also be paid to sectors that are often neglected by the agrometeorologist, such as those occurring in plant and animal protection. In addition, the borders between meteorological forecasts for agriculture and agrometeorological forecasts are not always clear. Examples include the use of weather forecasts for farm operations such as spraying pesticides or deciding on trafficability in relation to adverse weather. Many forecast issues by various national institutions (weather, but also commodity prices or flood warnings) are vital to the farming community, but they do not constitute agrometeorological forecasts. (Modified From the introduction of the chapter: Scope of agrometeorological forecasting)JRC.H.4-Monitoring Agricultural Resource