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

Distribution map of Ambrosia artemisiifolia L. (Asteraceae) in Italy

The spread of the invasive and allergenic Ambrosia artemisiifolia L. in Italy was analysed and mapped using distribution data from a wide range of sources. Ambrosia artemisiifolia occupies 1057 floristic quadrants which are mostly distributed in the Po plain. The distribution obtained represents the basis to implement urgent management strategies

Quantifying the accuracy of digital hemispherical photography for LAI estimates on broad-leaved tree species

Digital hemispherical photography (DHP) has been widely used to estimate leaf area index (LAI) in forestry. Despite the advancement in the processing of hemispherical imageswith dedicated tools, several steps are still manual and thus easily affected by user\u2019s experience and sensibility. The purpose of this study was to quantify the impact of user\u2019s subjectivity on DHP LAI estimates for broad-leaved woody canopies using the software Can-Eye. Following the ISO 5725 protocol, we quantified the repeatability and reproducibility of themethod, thus defining its precision for a wide range of broad-leaved canopies markedly differing for their structure. To get a complete evaluation of the method accuracy, we also quantified its trueness using artificial canopy images with known canopy cover. Moreover, the effect of the segmentationmethod was analysed. The best results for precision (restrained limits of repeatability and reproducibility) were obtained for high LAI values (>5) with limits corresponding to a variation of 22% in the estimated LAI values. Poorer results were obtained formediumand low LAI values, with a variation of the estimated LAI values that exceeded the 40%. Regardless of the LAI range explored, satisfactory results were achieved for trees in row-structured plantations (limits almost equal to the 30% of the estimated LAI). Satisfactory resultswere achieved for trueness, regardless of the canopy structure. The paired t-test revealed that the effect of the segmentationmethod on LAI estimates was significant. Despite a non-negligible user effect, the accuracymetrics for DHP are consistent with those determined for other indirectmethods for LAI estimates, confirming the overall reliability of DHP in broad-leaved woody canopies

Ecological and biodiversity gradients across alpine dry grassland habitats: implications for an endangered species

Dry grasslands are of great interest for nature conservation in Europe, because they have a central role in the conservation of numerous rare and endangered species. In this study carried out in the Brenta mountain group (Italian alps), we investigated the effect of environmental factors mainly controlled by topography, on the biodiversity trends across different dry grassland habitats where the threatened alpine stenoendemic Erysimum aurantiacum grows. Plant community data and ecological factors were analysed by means of a multi-habitat CCA approach and by analysis of biodiversity gradients in 7 natural and semi-natural habitats. We found that species turnover and biodiversity patterns vary as a function of multi-factorial ecological gradients. For the single habitats, elevation gradient was the main factor explaining compositional variation, followed by inclination and proportion of exposed rock surface. Despite its endangered status, E. aurantiacum showed a relatively high degree of ecological plasticity across these semiarid grassland habitats that probably allows it to survive in different environments, including in some cases those impacted by human activities. This prompts for habitat- more than species-level conservation actions. According to their characteristics and threats, habitat-specific management practices are recommended for long term conservation of plant species communities in the different ecological niches

Identification and expression pattern of mago nashi during zebrafish development

In a search for zebrafish genes expressed during early stages of development, we have identified two ESTs encoding proteins related to Drosophila mago nashi. Zebrafish mago nashi codes for a small protein with no clearly identified functional domains, and which is highly conserved during evolution. This paper describes the identification and a detailed gene expression analysis of zebrafish mago nashi during development. Our results demonstrate that mago nashi encodes a maternal transcript detected in both blastomeres and yolk cell at the 1\u20132 cell stages, and in the blastoderm during segmentation. We show that a putative microtubule-mediated transport of mago nashi mRNA from the vegetal hemisphere into animal blastomeres determines the localization of the transcript in the animal pole, immediately after fertilization. Furthermore, the microtubule array contained into the yolk cell seems to be responsible for the high level of mago nashi transcript detected in the central blastomeres at the 8\u201316 cell stages. Zygotic mago nashi is expressed into the dorsal-marginal region during gastrulation, and starting from somitogenesis to 24 hpf, the expression domain becomes progressively restricted to the developing neural tube and paraxial structures, and ventrally to the pronephric ducts. q 2004 Elsevier B.V. All rights reserved

An improved version of WOFOST for the simulation of quantitative and qualitative aspects of winter rapeseed production

Rapeseed (Brassica napus L.) is one of the most widespread oleaginous crop. Its importance is expected to increase in Europe in the coming years, since the Renewable Energy Directive set for EU Countries the goal of supplying the 10% of transport fuel with renewable sources in 2020 (OECD-FAO, 2011). This aim will likely encourage the cultivation of rapeseed, the most important oilseed crop in Europe for biodiesel production. In this context, crop models can be effective tools in supporting the bioenergy sector, since they can be used to identify the most suitable lands for the cultivation of a specific crop, to optimize the agro-management and to forecast quantitative and qualitative aspect of productions. WOFOST (van Keulen and Wolf, 1986) is a widely used generic crop model appreciated for the high level of detail used to reproduce crop growth and development. On the other hand, it is characterized by a large number of parameters, in turn leading to time consuming parameterization procedures. Moreover, a large part of these parameters are organized in AFGEN (Arbitrary Function GENerator) tables which describe the dependence of parameters on air temperature or development stage. Given their high flexibility, these tables could allow the user to fit unrealistic functions to describe plant processes. Finally WOFOST \u2013 being a generic model \u2013 is not able to reproduce the specific processes characterizing some crops, especially rapeseed. In order to overcome these limitations, we formalized a specific crop model (i.e., WOFOST_Rapeseed) by improving and extending the original version of the model. The development of the new model targeted (i) the reduction of model complexity via the use of crop-specific functions driven by few parameters with a biophysical meaning to eliminate AFGEN tables, (ii) the improvement of leaf area dynamics within the vertical profile of the canopy and (iii) the implementation of approaches for yield quality. An improved version of WOFOST for the simulation of quantitative and qualitative aspects of winter rapeseed production

Neurogenic role of prox1 in the CNS and lateral line development

In teleost, the lateral line is a sensory organ composed of neuromasts containing hair cells, expressing athl, and surrounded by supporting cells, expressing notch3. notch3 signalling seems to limit the number of cells that are allowed to adopt the hair cell fate while failure of notch3 signal generates an overproduction of athl expressing hair cells in the middle of the neuromast. In this report we analyze the role of prox1 in zebrafish (z-prox1) on neural and proneural genes in the neuromasts of the posterior lateral line. We have examinated the z-prox1 interaction with ath1 and notch3 in the lateral line system of zebrafish by both z-prox1 morpholino-mediated inactivation and z-prox1 mRNA overexpression. This gene is expressed in the migrating primordium, and its inactivation results in a reduced number of neuromasts at 48 hpf but does not affect the primordium migration. In particular, lack of prox1 inhibits the differentiation of ath1 expressing hair cells, while enhances the number of the supporting cells, expressing notch3. Injection of prox1 synthetic mRNA generates the opposite phenotype: the number of the pre-determined ath1 expressing hair cells is increased, while notch3 expression in the supporting cells is reduced. Moreover prox1 could have a role in timing regulation of the neuromasts development because morphant embryos rigenerate neuromasts after 48 hpf

WOFOST-GTC : A new model for the simulation of winter rapeseed production and oil quality

Rapeseed is one of the most important sources of vegetable oils, and its cultivation in Europe is expanding due to the economic incentives to grow energy crops. Given the unique characteristics of this crop, simulation studies targeting yield predictions and scenario analysis should be performed using specific models rather than using generic crop simulators adapted to rapeseed via calibration. This study presents a new model \u2013 WOFOST-GTC \u2013 which implements a dynamic representation of the rapeseed canopy architecture and includes modelling approaches to simulate oil content and composition. We reduced the number of model parameters to 35, compared to the 97 parameters of the original WOFOST model, from which it derives. WOFOST-GTC was developed using data collected in dedicated field experiments carried out in northern Italy in 2012\u20132013. The model ability to reproduce the underlying processes was evaluated using data collected in Europe between 1993 and 2013. In particular, dynamics involved with production and oil quality were evaluated on 7 and 18 datasets, respectively. The aboveground biomass and photosynthetic area index at different depths in the canopy were accurately simulated (R2\ua0=\ua00.86 and 0.78, respectively). Despite the lower complexity, WOFOST-GTC proved to be as accurate as the original WOFOST model. The simulation of the seed oil content (R2\ua0=\ua00.76) and of the oleic (R2\ua0=\ua00.95), linoleic (R2\ua0=\ua00.88) and \u3b1-linolenic acid (R2\ua0=\ua00.95) fractions was accurate. Hence, we propose WOFOST-GTC as a suitable simulation model to analyse the rapeseed production and oil quality under different weather and management scenarios

Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data

Crop modeling and remote sensing are key tools to gain deeper understanding on cropping system dynamics and, ultimately, to increase the sustainability of agricultural productions. This study presents a system to estimate rice yields at sub-field scale based on the integration of a biophysical model and remotely sensed products. Leaf area index (LAI) data derived from decametric optical imageries (i.e., Landsat-8, Landsat-7 and Sentinel\u20132A) were assimilated into the WARM rice model via automatic recalibration of crop parameters at a fine spatial resolution (30 m 7 30 m), targeting the lowest error between simulated and remotely sensed LAI. The performance of the system was evaluated by comparing simulated yield using default and recalibrated parameters at sub-field scale with yield maps generated by a GPS-equipped harvester. The training dataset included 40 paddy fields in Northern Italy, which were sampled during three cropping seasons, from 2014 to 2016. The assimilation of remotely sensed LAI into model parameters increased the accuracy of the system: MAE and RRMSE were 0.66 t ha-1 [CI: 0.54 t ha-1\u20130.78 t ha-1] and 13.8% [CI: 11.7%\u201315.7%], respectively, whereas they were 0.82 t ha-1 [CI: 0.68 t ha-1\u20130.96 t ha-1) and 15.7% [CI: 14.1%,\u201317.4%] without assimilation. Moreover, the system allowed to properly reproduce the within-field yield variability, thus laying the basis for possible applications in precision agriculture advisory services