CLIMA: a weather generator framework

Abstract: Weather generators (WG) can be defined as collections of models to estimate site specific weather data and derived variables. Their use spans from providing inputs to a variety of biophysical models to deriving weather indices. Also, using either global circulation models or local area models inputs, sets of parameters calculated from long term weather series specific to a site can be modified to reproduce via WG synthetic series representing climate change scenarios. Finally, models implemented in WG are used for estimating missing data and to perform quality control on data collected from sensors in weather stations. The models implemented in WG vary from purely empirical to physically based. There are several models to either estimate or to generate each weather variable, with different input requirements. New models are continuously being proposed, and, whether some models to estimate specific variables are commonly accepted as reference methods, the lack of some inputs requires at times using alternate approaches. Currently available WG are applications which implement a predefined set of modelling options, in software implementations which do not allow for independent extensions by third parties. The CLIMA weather generator is a component based application which consist of a set of reusable graphical user interface (GUI) components, and of a set of extensible model components. The latter are subdivided into six namespaces to estimate variables related to air temperature, rainfall, solar radiation, evapotranspiration, wind, and leaf wetness. The time characteristic of the variables estimated varies from a day to ten minutes. Another library allows estimating climatic indices from one year of daily data at the time. The current implementation consists of a total of more than 300 models. Components are usable either via the CLIMA GUI, or via custom developed applications in a client-server architecture. The architecture of components is based on the composite and strategy as keystone design patterns. Models are implemented as single approaches (simple strategies), and as composite models (composite strategies) which are associated to models of finer granularity. Another type of model unit is represented by context strategies, which implement logic to select within associated models. Finally, the GUI allows building composite models which can be saved as libraries, to be reused both within CLIMA for weather series generation, or independently by other applications. The components are implemented as .NET libraries. They implement the test of pre- and post-conditions, and a scalable tracing via .NET listeners. All variables and parameters are documented via a description, units, default, maximum, and minimum values. Components are extensible: new models can be added independently by third parties and detected by the CLIMA application, which can also use them for data generation via building new composite libraries. Each component is made available via a software development kit which includes the code of two sample projects, either to extend or to reuse the component. CLIMA and its model components are freely available for reuse in no-profit applications.JRC.DG.G.3-Monitoring agricultural resource

Impact of Climate Variations on Soybean Yield in Eastern Arkansas: 1960-2014

Climate is the major factor affecting crop production; therefore, various agro-meteorological indicators have been frequently used to evaluate the impact of climate on crop production. In this study, we examined the temporal variations of agrometeorological indicators (growing degree days, total precipitation, dry spells and drought indices) during 1960-2014 and their impact on soybean yields in East Arkansas. Results show an increasing trend in growing degree days (GDDs) and dry spells, though the total precipitation during the soybean growing season remained nearly unchanged during the study period. Generally, GDDs and dry spells show a strong correlation with yields. We also evaluated drought variability based on different drought indices, including the Palmer Drought Severity Index (PDSI), the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). The drought indices are all negatively correlated to soybean yields. Overall, the one month SPEI showed the strongest impact on yields. After regression analysis, Dry spells and Total precipitation were the only significant factors in the General Linear Model (GLM)

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils

ACKNOWLEDGEMENTS This study was supported by the project “C and N models inter-comparison and improvement to assess management options for GHG mitigation in agro-systems worldwide” (CN-MIP, 2014- 2017), which received funding by a multi-partner call on agricultural greenhouse gas research of the Joint Programming Initiative ‘FACCE’ through national financing bodies. S. Recous, R. Farina, L. Brilli, G. Bellocchi and L. Bechini received mobility funding by way of the French Italian GALILEO programme (CLIMSOC project). The authors acknowledge particularly the data holders for the Long Term Bare-Fallows, who made their data available and provided additional information on the sites: V. Romanenkov, B.T. Christensen, T. Kätterer, S. Houot, F. van Oort, A. Mc Donald, as well as P. Barré. The input of B. Guenet and C. Chenu contributes to the ANR “Investissements d’avenir” programme with the reference CLAND ANR-16-CONV-0003. The input of P. Smith and C. Chenu contributes to the CIRCASA project, which received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no 774378 and the projects: DEVIL (NE/M021327/1) and Soils‐R‐GRREAT (NE/P019455/1). The input of B. Grant and W. Smith was funded by Science and Technology Branch, Agriculture and Agri-Food Canada, under the scope of project J-001793. The input of A. Taghizadeh-Toosi was funded by Ministry of Environment and Food of Denmark as part of the SINKS2 project. The input of M. Abdalla contributes to the SUPER-G project, which received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no 774124.Peer reviewedPostprin

Agro-Climatic Change, Crop Production and Mitigation Strategies-Case Studies in Arkansas, USA and Kenya

Although climate change impacts vary geographically and temporally, studies at local levels are not readily available for stakeholders to better understand how their local communities would be affected and what remedial measures could be more effective in their local contexts. This dissertation has examined climate change and its impacts in two different local contexts: eastern Arkansas in the USA and Nyando in Kenya. The first part of this dissertation develops agro-meteorological indicators and examines the relationship between agro-meteorological indicators and crop yields in eastern Arkansas between 1960 and 2014. Results reveal that temperature based indicators were more strongly correlated to crop yield than precipitation based indicators. However, drought indices also performed very well. The second part projects future climate scenarios in eastern Arkansas using the agro-meteorological indicators developed in the first part. Results show slight increases in total precipitation, extreme precipitation and lengthening growing season duration. The last part identifies the socio-economic factors affecting the Agro-forestry (AFR) technology adoption in Kenya. Results reveal that farmers with more land, more income and/or more education are more likely to adopt agro-forestry technologies. Years of residence, access to information and reliance on crop income also positively affect the likelihood of using AFR technology. This study is very critical for Kenya where the national forest cover is less than 3%

A software component to compute agro-meteorological indicators

ClimIndices is a software component to compute agro-meteorological indicators on yearly series of daily weather data. The component is released as .NET2 dynamic link libraries (DLL), allowing the development of clients under Windows using .NET languages. The design allows extending the computing capabilities without requiring re-compilation

IMPROVING DECISION SUPPORT TOOLS VIA INTEGRATION OF REMOTELY SENSED DATA IN CROP MODELS

La necessit\ue0 di garantire l\u2019accesso al cibo per una popolazione mondiale in continua crescita, adempiendo al contempo a precisi requisiti ambientali, rappresenta una grande sfida per il settore agroalimentare. Il successo, in questa sfida, pu\uf2 essere garantito da un utilizzo ottimale delle risorse aziendali, raggiungibile attraverso sforzi notevoli volti al monitoraggio e all\u2019analisi del sistema agricolo. I recenti sviluppi in campo satellitare e modellistico, forniscono strumenti adatti allo scopo specialmente in caso di un utilizzo integrato delle due tecnologie. Il progetto di ricerca, oggetto di questa tesi, mira alla formalizzazione e successiva valutazione di uno strumento, basato sull\u2019integrazione di modelli di simulazione e telerilevamento, per il supporto alle decisioni in agricoltura. L\u2019attivit\ue0 di ricerca, culminata nell\u2019applicazione dello strumento al caso studio, ha previsto la realizzazione di due attivit\ue0 preliminari. In prima istanza \ue8 stato eseguito uno studio di analisi di sensibilit\ue0 per accrescere la conoscenza del modello utilizzato e identificare i parametri, alla cui variazione, il modello risulta pi\uf9 sensibile. In questo studio sono state considerate sia serie climatiche attuali che proiezioni nel medio futuro e, utilizzando due diverse configurazioni del modello WOFOST (una standard ed una che permette di simulare l\u2019impatto degli eventi meteorologici estremi sulle colture), sono state simulate le cinque colture pi\uf9 coltivate in Europa. I risultati evidenziano una forte sensibilit\ue0 del modello ai parametri coinvolti nella simulazione degli organi di accumulo in quasi tutte le condizioni esplorate a meno dei casi in cui sono state riscontrate condizioni limitanti per la produttivit\ue0 delle colture. In queste condizioni il modello \ue8 infatti risultato pi\uf9 sensibile ai parametri che regolano la simulazione delle prime fasi di crescita delle colture. La seconda attivit\ue0 preliminare ha invece permesso di quantificare l\u2019impatto della componente soggettiva sulla precisione delle stime di indice di area fogliare (LAI), una variabile tra le pi\uf9 utilizzate per permettere l\u2019integrazione di modelli di simulazione e telerilevamento. Attraverso l\u2019applicazione del protocollo previsto dalla normativa ISO 5725 \ue8 stato possibile calcolare i limiti di ripetibilit\ue0 e riproducibilit\ue0 delle stime di LAI da immagini emisferiche e quindi ottenere una misura della loro precisione. I risultati ottenuti, dimostrano l\u2019affidabilit\ue0 della tecnica seguita per ottenere stime di LAI; la precisione ottenuta \ue8 stata infatti comparabile a quella che caratterizza altri strumenti in commercio. I risultati migliori sono stati ottenuti in caso di coperture vegetali continue ed omogenee, caratteristiche dei sistemi agricoli intensivi, sottolineando ulteriormente l\u2019affidabilit\ue0 di tale tecnica in questi contesti. Entrambe le attivit\ue0, qui brevemente riassunte, hanno permesso di definire un valido punto di partenza per l\u2019integrazione di modellistica e telerilevamento fornendo informazioni utili per la progettazione e la realizzazione del caso studio. In questa ultima attivit\ue0, un sistema di previsione ad alta risoluzione basato sull\u2019integrazione di modellistica e telerilevamento \ue8 stato formalizzato e quindi valutato utilizzando dati raccolti in risaia durante le annate 2014, 2015 e 2016. Il modello colturale WARM \ue8 stato integrato con serie temporali di LAI telerilevate, ricalibrando automaticamente quei parametri, identificati come i pi\uf9 influenti oppure strettamente legati alla simulazione del LAI. Il confronto dei risultati ottenuti adottando questo approccio con quelli ottenuti utilizzando solamente il modello colturale, ha permesso di evidenziare i miglioramenti nella stima della produttivit\ue0 del riso dovuti all\u2019integrazione di informazioni telerilevate. In generale la simulazione delle produttivit\ue0 del riso \ue8 risultata affetta da un ridotto RRMSE (13.8%), se confrontata con quella ottenuta usando solamente il modello (RRMSE = 15.7%). Inoltre l\u2019integrazione delle due tecnologie ad una elevata risoluzione spaziale (30 m 7 30 m), ha consentito di riprodurre la variabilit\ue0 interna di ciascun campo. I risultati ottenuti evidenziano la validit\ue0 del sistema proposto per la stima della produttivit\ue0 del riso ad un\u2019elevata risoluzione spaziale. Ci\uf2 detto, durante la valutazione del sistema sono emerse alcune criticit\ue0 legate ad incongruenze tra le variabili simulate e quelle telerilevate. Questi aspetti, cos\uec come la possibilit\ue0 di considerare altre colture e altri modelli di simulazione, pongono le basi per ricerche future.The need to fulfil sustainability requirements while increasing productions to feed the raising world\u2019s population represents a big challenge for the agricultural sector. To achieve this goal, improving management of resources at farm level is acknowledged as one of the most effective solutions. However, this requires intensive activities targeting cropping system monitoring and data processing. The advances in remote sensing and simulation technologies \u2013 especially when used in an integrated way \u2013 provide a valuable solution to support farmers and technicians in such a context. This research aims at setting up and evaluating pre-operational tools based on the integration of crop models and remotely sensed information to support decision making in cropping systems management. The research was articulated in two main preliminary activities before the application of crop models and remote sensing in a case study. Extensive sensitivity analysis experiments were performed to deepen the knowledge about model behaviour and to identify the most influential parameters for yield simulation. A wide range of conditions was investigated, considering both current weather and future climate projections, as well as five major crops cultivated in several European sites and using two different modelling solutions (the standard version of WOFOST and a version of the model improved for the simulation of the impact of extreme weather events). Model outputs were mainly influenced by parameters involved with storage organs development; nevertheless, in case limiting conditions were explored, simulations were influenced by parameters driving crop growth during early stages. Given leaf area index (LAI) data are those mostly used when crop models and remote sensing are integrated, the second activity targeted the quantification of the impact of subjectivity in LAI estimates from hemispherical images. Precision was determined via the application of the ISO 5725 validation protocol, thus leading to define repeatability and reproducibility limits. Results proved the reliability of LAI estimates from hemispherical images; the precision obtained was indeed comparable with that of other commercial instruments. The best results were obtained in case of high LAI and continuous canopy, further underlying the reliability of this method for intensive agricultural systems characterized by continuous and homogeneous canopies. Both the activities just presented aimed at defining a sound starting point for the coupling of crop models and remote sensing, providing useful information for the design of the case study. For this last activity, a high-resolution pre-operational system based on the WARM model and remotely sensed information was evaluated using observations from paddy rice fields during the seasons 2014, 2015 and 2016. The remotely sensed information, consisting in temporal series of LAI, were integrated in the model by automatically re-calibrating either parameters identified as the most influential or those strictly related with LAI dynamics. The system performances were compared with those obtained using the default parameterizations of the model. Results underlined the improvement in rice yield simulation after the integration of remotely sensed data, proving the reliability of the system. Overall, the simulation of rice yield was affected by a restrained RRMSE (13.8%), compared to the results obtained with the default model parameterizations (RRMSE = 15.7%). Moreover, the assimilation of remotely sensed information at high spatial resolution (30 m 7 30 m) led to satisfactorily describe the within-field yield variability. The obtained results make the proposed system a valuable solution to provide high-resolution estimates of rice productivity. Nonetheless, weakness were highlighted, related with some the inconsistencies between observed crop state variables and crop reflectance properties. This, as well as the possibility to consider other models and crops, lays the basis for further studies