A Comprehensive Modeling Approach for Crop Yield Forecasts using AI-based Methods and Crop Simulation Models

Numerous solutions for yield estimation are either based on data-driven models, or on crop-simulation models (CSMs). Researchers tend to build data-driven models using nationwide crop information databases provided by agencies such as the USDA. On the opposite side of the spectrum, CSMs require fine data that may be hard to generalize from a handful of fields. In this paper, we propose a comprehensive approach for yield forecasting that combines data-driven solutions, crop simulation models, and model surrogates to support multiple user-profiles and needs when dealing with crop management decision-making. To achieve this goal, we have developed a solution to calibrate CSMs at scale, a surrogate model of a CSM assuring faster execution, and a neural network-based approach that performs efficient risk assessment in such settings. Our data-driven modeling approach outperforms previous works with yield correlation predictions close to 91\%. The crop simulation modeling architecture achieved 6% error; the proposed crop simulation model surrogate performs predictions almost 100 times faster than the adopted crop simulator with similar accuracy levels

Vegetation dynamics in northern south America on different time scales

The overarching goal of this doctoral thesis was to understand the dynamics of vegetation activity occurring across time scales globally and in a regional context. To achieve this, I took advantage of open data sets, novel mathematical approaches for time series analyses, and state-of-the-art technology to effectively manipulate and analyze time series data. Specifically, I disentangled the longest records of vegetation greenness (>30 years) in tandem with climate variables at 0.05° for a global scale analysis (Chapter 3). Later, I focused my analysis on a particular region, northern South America (NSA), to evaluate vegetation activity at seasonal (Chapter 4) and interannual scales (Chapter 5) using moderate spatial resolution (0.0083°). Two main approaches were used in this research; time series decomposition through the Fast Fourier Transformation (FFT), and dimensionality reduction analysis through Principal Component Analysis (PCA). Overall, assessing vegetation-climate dynamics at different temporal scales facilitates the observation and understanding of processes that are often obscured by one or few dominant processes. On the one hand, the global analysis showed the dominant seasonality of vegetation and temperature in northern latitudes in comparison with the heterogeneous patterns of the tropics, and the remarkable longer-term oscillations in the southern hemisphere. On the other hand, the regional analysis showed the complex and diverse land-atmosphere interactions in NSA when assessing seasonality and interannual variability of vegetation activity associated with ENSO. In conclusion, disentangling these processes and assessing them separately allows one to formulate new hypotheses of mechanisms in ecosystem functioning, reveal hidden patterns of climate-vegetation interactions, and inform about vegetation dynamics relevant for ecosystem conservation and management

Global mapping of volumetric water retention at 100, 330 and 15 000 cm suction using the WoSIS database

Present global maps of soil water retention (SWR) are mostly derived from pedotransfer functions (PTFs) applied to maps of other basic soil properties. As an alternative, ‘point-based’ mapping of soil water content can improve global soil data availability and quality. We developed point-based global maps with estimated uncertainty of the volumetric SWR at 100, 330 and 15 000 cm suction using measured SWR data extracted from the WoSIS Soil Profile Database together with data estimated by a random forest PTF (PTF-RF). The point data was combined with around 200 environmental covariates describing vegetation, terrain morphology, climate, geology, and hydrology using DSM. In total, we used 7292, 33 192 and 42 016 SWR point observations at 100, 330 and 15 000 cm, respectively, and complemented the dataset with 436 108 estimated values at each suction. Tenfold cross-validation yielded a Root Mean Square Error (RMSE) of 6.380, 7.112 and 6.485 10−2cm3cm−3, and a Model Efficiency Coefficient (MEC) of 0.430, 0.386, and 0.471, respectively, for 100, 330 and 15 000 cm. The results were also compared to three published global maps of SWR to evaluate differences between point-based and map-based mapping approaches. Point-based mapping performed better than the three map-based mapping approaches for 330 and 15 000 cm, while for 100 cm results were similar, possibly due to the limited number of SWR observations for 100 cm. Major sources or uncertainty identified included the geographical clustering of the data and the limitation of the covariates to represent the naturally high variation of SWR

Modelling Species Distributions with Deep Learning to Predict Plant Extinction Risk and Assess Climate Change Impacts

The post-2020 global biodiversity framework needs ambitious, research-based targets. Estimating the accelerated extinction risk due to climate change is critical. The International Union for Conservation of Nature (IUCN) measures the extinction risk of species. Automatic methods have been developed to provide information on the IUCN status of under-assessed taxa. However, these compensatory methods are based on current species characteristics, mainly geographical, which precludes their use in future projections. Here, we evaluate a novel method for classifying the IUCN status of species benefiting from the generalisation power of species distribution models based on deep learning. Our method matches state-of-the-art classification performance while relying on flexible SDM-based features that capture species' environmental preferences. Cross-validation yields average accuracies of 0.61 for status classification and 0.78 for binary classification. Climate change will reshape future species distributions. Under the species-environment equilibrium hypothesis, SDM projections approximate plausible future outcomes. Two extremes of species dispersal capacity are considered: unlimited or null. The projected species distributions are translated into features feeding our IUCN classification method. Finally, trends in threatened species are analysed over time and i) by continent and as a function of average ii) latitude or iii) altitude. The proportion of threatened species is increasing globally, with critical rates in Africa, Asia and South America. Furthermore, the proportion of threatened species is predicted to peak around the two Tropics, at the Equator, in the lowlands and at altitudes of 800-1,500 m.Comment: 18 pages, 5 figures. Coda and data: https://github.com/estopinj/IUCN_classificatio

Seasonal Biotic Processes Vary the Carbon Turnover by Up To One Order of Magnitude in Wetlands

Soil Organic Carbon (SOC) turnover t in wetlands and the corresponding governing processes are still poorly represented in numerical models. t is a proxy to the carbon storage potential in each SOC pool and C fluxes within the whole ecosystem; however, it has not been comprehensively quantified in wetlands globally. Here, we quantify the turnover time t of various SOC pools and the governing biotic and abiotic processes in global wetlands using a comprehensively tested process-based biogeochemical model. Globally, we found that t ranges between 1 and 1,000 years and is controlled by anaerobic (in 78% of global wetlands area) and aerobic (15%) respiration, and by abiotic destabilization from soil minerals (5%). t in the remaining 2% of wetlands is controlled by denitrification, sulfur reduction, and leaching below the subsoil. t can vary by up to one order of magnitude in temperate, continental, and polar regions due to seasonal temperature and can shift from being aerobically controlled to anaerobically controlled. Our findings of seasonal variability in SOC turnover suggest that wetlands are susceptible to climate-induced shifts in seasonality, thus requiring better accounting of seasonal fluctuations at geographic scales to estimate C exchanges between land and atmosphere

AI-based Mapping of the Conservation Status of Orchid Assemblages at Global Scale

Although increasing threats on biodiversity are now widely recognised, there are no accurate global maps showing whether and where species assemblages are at risk. We hereby assess and map at kilometre resolution the conservation status of the iconic orchid family, and discuss the insights conveyed at multiple scales. We introduce a new Deep Species Distribution Model trained on 1M occurrences of 14K orchid species to predict their assemblages at global scale and at kilometre resolution. We propose two main indicators of the conservation status of the assemblages: (i) the proportion of threatened species, and (ii) the status of the most threatened species in the assemblage. We show and analyze the variation of these indicators at World scale and in relation to currently protected areas in Sumatra island. Global and interactive maps available online show the indicators of conservation status of orchid assemblages, with sharp spatial variations at all scales. The highest level of threat is found at Madagascar and the neighbouring islands. In Sumatra, we found good correspondence of protected areas with our indicators, but supplementing current IUCN assessments with status predictions results in alarming levels of species threat across the island. Recent advances in deep learning enable reliable mapping of the conservation status of species assemblages on a global scale. As an umbrella taxon, orchid family provides a reference for identifying vulnerable ecosystems worldwide, and prioritising conservation actions both at international and local levels.Comment: 21 pages, 4 figures. Website URL: https://mapviewer.plantnet.org/?config=apps/store/orchid-status.xml Data and code: https://figshare.com/s/15404886eb3b62363a5

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms

Ecological genomics and adaptation of rosewoods Dalbergia cochinchinensis and D. oliveri for conservation and restoration

Global biodiversity, in particular tropical forests, is decreasing under both environmental change and anthropogenic disturbance. Environmental change alters species’ adaptability to their current habitat, leading to loss of fitness and range shift, while anthropogenic disturbance reduces their adaptive capacity. Conserving and restoring threatened species and ecosystems thus become a grand challenge for the 21st century. This thesis studies two threatened rosewood species, Dalbergia cochinchinensis and D. oliveri, which are illegally exploited for their valuable timber in the Greater Mekong Subregion. They became the world’s most trafficked wild product between 2005 and 2014, amounting to ~40% of the total global trade. Conservation efforts grew in the last decade to tackle the range-wide challenge, aiming to improve the species’ survival, amplify the production of genetic materials, and designate more conservation units. However, a sustainable supply of genetic materials can meet several challenges that compromise the effectiveness of a restoration programme, namely the genetic bottlenecks, maladaptation, and climate change. While knowledge of adaptation can predict and mitigate these risks, standard study approaches such as common garden experiments have become impractical due to the acute threats of illegal logging in these two species, which are lacking in a priori knowledge. This thesis aims to increase the knowledge of genetic and physiological underpinning of adaptation in the two Dalbergia species with relevance to application in conservation and restoration strategies. This thesis presents a rich body of genomic resources such as chromosome-scale genomes and reference transcriptomes, which advance the progress in less-represented angiosperm tree genomes and woody legume genomes and enable studies in genetic diversity. Comparative genomic studies revealed insight into the evolution and potential adaptive role of of certain gene families in tropical Dalbergia species. The landscape genomic study provides a comprehensive scan of adaptive signals and reports significant differences of the adaptive variation between the two species, where D. cochinchinensis is driven by temperature variability and D. oliveri by precipitation variability. The controlled stress experiment provides a physiological understanding of how the two species regulate their water relations and photosynthetic apparatus to respond to drought differently, where D. cochinchinensis has a more anisohydric behaviour than D. oliveri. These contrasting patterns of adaptation indicate how the two species may differentiate their niches, while co-occurring in some habitats. The knowledge of adaptive variation identifies hotspots of local adaptation and vulnerability towards climate change, and thus are expected to help conservation practitioners delineate conservation units, compare provenances for assisted germplasm transfer, and prioritise conservation actions. It also opens new avenues for future research, including combining common garden experiments and genomic approaches to more fully unravel genotype-phenotype-environment relationships

Soil erosion in Sicily: testing hydro-morphological approaches

I processi di denudazione sulla Terra sono fenomeni naturali di modellazione della sua superficie sotto l'azione di una serie di agenti, tra i quali il deflusso delle acque superficiali svolge un ruolo centrale soprattutto nelle zone climatiche umide. Ogni processo di modellazione è caratterizzato da tre fasi: erosione, trasporto e deposito. Nel caso di fenomeni legati al deflusso dell'acqua, devono essere analizzati in un sistema molto complesso, l’acqua infatti scorre con modalità diverse dai versanti (overland flow) ai ruscelli (rete di drenaggio) ai fiumi (canali). Allo stesso tempo, la litosfera espone al lavoro meccanico dell'acqua diversi tipi di geo-materiali, che vanno da rocce cementate molto resistenti a terreni e detriti molto deboli. Nel caso delle rocce s.s. le differenze in termini di erodibilità sono legate ai processi litogenetici (composizione, tessitura, struttura) e ai successivi fenomeni atmosferici che ne possono modificare lo stato originale. Gran parte della Terra è in realtà "coperta" dai prodotti degli agenti atmosferici o dai depositi dei processi di modellazione (detriti s.s., in geomorfologia). Lo strato mediamente stabile è l'orizzonte di base dove la vegetazione cresce e fissa l'eluvium (suolo s.s., in geomorfologia). I depositi detritici sono il risultato dell'azione erosiva dell'agente modellante e possono avere caratteristiche molto diverse a seconda del processo genetico specifico.Secondo gli elementi molto generali menzionati sopra, l'approccio allo studio dell'erosione mette lo scienziato di fronte a una sfida molto complessa. Tuttavia, un approccio semplificato può fornire strumenti per comprendere e modellare i processi in misura sufficiente per affrontare le esigenze attuali. Infatti, se la logica naturale dei processi di erosione è indiscutibile, gli effetti legati all'incrocio di questi processi e l'attività umana sono di interesse in quanto sono correlati ad alcuni aspetti di rischio. La perdita del suolo è ovviamente una fonte diretta di danni per l'agricoltura e le coltivazioni. Il deterioramento del suolo e dei detriti può provocare fenomeni distruttivi come debris floods e debris flow o ridurre la sezione del canale disponibile sui ponti e riempire i serbatoi artificiali riducendone la loro capacità di stoccaggio.Questa tesi cerca di indagare le problematiche dell'erosione del suolo in Sicilia, focalizzandosi su tre principali approcci.Il primo approccio si basa sull'analisi dei processi di erosione da una prospettiva sperimentale, cercando le relazioni tra la portata liquida e la portata solida, misurati in sezioni fluviali, e le caratteristiche idro-morfodinamiche dei bacini idrologici. In particolare, i dati storici della rete regionale delle stazioni di misura idrometriche sono stati analizzati costruendo le Sediment Rating Curves (SRC), che predicono la concentrazione del sedimento in sospensione (Suspended Sediment Concentration) dalla portata liquida (Q), e analizzando i cicli annuali di isteresi media. Un approccio diverso è stato adottato nel secondo caso sperimentale, in cui SWAT (Soil and Water Assessment Tool), un modello continuo fisicamente basato a scala di bacino e sviluppato dall'USDA-ARS, è stato applicato al bacino del fiume San Leonardo. L'obiettivo di SWAT è determinare in che modo l'uso e la gestione del territorio possano influire sulle portate liquide e sulle portate solide nei bacini idrici. In SWAT, l'erosione e la resa dei sedimenti sono stimati dalla Modified Universal Soil Loss Equation (MUSLE). Mentre la più conosciuta ed applicata USLE (Universal Soil Loss Equation) utilizza la pioggia come indicatore dell'energia erosiva, la MUSLE utilizza la quantità di deflusso funzione delle condizioni di umidità antecedenti e dell'energia delle precipitazioni. Nella terza ed ultima applicazione, la valutazione dell'erosione idrica è stata effettuata nell'ambito di uno studio di vulnerabilità all’erosione costiera per stimare il contributo dell’apporto di sedimento nelle unità fisiografiche costiere siciliane. In particolare, il modello spazialmente distribuito WaTEM/SEDEM è stato utilizzato per valutare il carico sedimentario trasportato nelle zone costiere. Il modello WaTEM/SEDEM è costituito da tre componenti principali: valutazione dell'erosione del suolo, calcolo della capacità di trasporto dei sedimenti e applicazione in un algoritmo di trasporto dei sedimenti. In WaTEM/SEDEM l'erosione del suolo è calcolata con una versione modificata della RUSLE. Una volta che il tasso medio annuale di erosione è noto, l’algoritmo viene utilizzato per trasferire la quantità di suolo prodotta (erosione lorda) dalla sorgente alla rete fluviale in base alla equazione della capacità di trasporto (TC in Mg yr-1).Denudation processes on Earth are intricate and natural modeling phenomena of its surface under the action of a set of agents, among which water runoff surface plays a central role in humid climatic areas. Each modeling process is marked by erosion s.s., transport, and deposit stages, which in the case of runoff water-related phenomena, have to be analyzed in a very complex system. In fact, running water flows with different modes from hillslopes (overland flow) to streams (drainage network) to rivers (channel). At the same time, the lithosphere is exposed to the mechanic work of running water with very different types of geo-materials, ranging from very (too) resistant cemented rocks to very weak soils and debris. In the case of rocks s.s., differences in terms of erodibility are linked to the lithogenetic processes (composition, texture, structure) and to subsequent weathering phenomena, which can modify the original status in the outcropping horizon volumes. A large part of the Earth is actually “covered” by the products of weathering processes or the deposits of the modeling processes (debris s.s., in geomorphology). The moderately stable weathered layer is the base horizon where vegetation grows and fixes the eluvium (soil s.s., in geomorphology). Debris deposits are the result of the erosive action of the modeling agent and can have very different characteristics according to the specific genetic process.According to the general elements above, approaching the study of erosion poses the scientist in front of a very intricate challenge. However, a simplified approach can furnish tools for understanding and modeling the processes to an extent suitable enough to face the current requirements. In fact, if the natural rationale of erosion processes is out of doubt, the effects of mixing these processes and human activity are of interest as some risk aspects are related. Soil loss is obviously a direct source of damage for agriculture and farming. Deteriorating soil and debris can result in destructive phenomena such as debris floods and debris flow, reducing the available channel section at bridges, or filling artificial reservoirs, reducing their storage capacity.This thesis tries to investigate water erosion issues in Sicily, with a specific focus on three main adopted approaches.The first approach is based on the analysis of the erosion processes from an experimental perspective, searching relations between the measured flow of water and sediment through a stream or fluvial section and the subtending hydro-morphodynamic, which merge in two time changing measured parameters all the effects of the interplay between rainfall/temperature forcing and slope, stream, and channel response.In particular, the available historical data of the hydrometric gauge stations' regional network are analyzed by modeling Sediment Rating Curves (SRCs), which predict the Suspended Soil Concentration (SSC) from the water discharge (Q) and analyzing the mean hysteresis annual loops. A different approach was adopted in the second experimental case, where SWAT (Soil and Water Assessment Tool), a physically-based continuous model for catchment scale simulations developed by the USDA-ARS, was applied to the San Leonardo River basin. SWAT's objective is to determine how land use and management can affect water, sediment, and agricultural chemical yields in ungauged watersheds. In SWAT, erosion and sediment yield are estimated from the Modified Universal Soil Equation (MUSLE). While the USLE uses rainfall as an indicator of erosive energy, MUSLE uses the amount of runoff, which should improve the sediment yield prediction because runoff is a function of antecedent moisture conditions as well as rainfall energy. Therefore, differently from USLE, delivery ratios are not needed with MUSLE because the runoff factor represents energy in detaching and transporting sediment.In a third application, water erosion assessment is carried out in the framework of a coastal erosion study to estimate the contribution of soil delivery at the coastal physiographic units in the advancement/retreatment stages. In particular, the spatially distributed model WaTEM/SEDEM is used to evaluate the sediment load carried to the coastal areas. The WaTEM/SEDEM model consists of three main components: soil erosion assessment, sediment transport capacity calculation, and sediment routing. Soil erosion is predicted with a modified version of RUSLE for 2-dimensional landscapes. Once the mean annual erosion rate is known at each grid cell, a routing algorithm is used to transfer the displaced soil amount (gross erosion) from the source to the river network according to the transport capacity (TC in Mg yr-1)