Agricultural transformations in the southern cone of Latin America: agricultural intensification and decrease of the aboveground net primary production, Uruguay’s case

This article belongs to the Special Issue Sustainable Farm Systems: Transitions in Agrarian Metabolism in the Past and Learning for the FutureThe agri-exporting enclaves of the current corporate food regime intensively exploit natural assets in global strategies that govern the local processes. Their multidimensional impacts contribute to the environmental and civilizational crisis. Linked to the agrarian metabolism in its appropriation phase, land use has impacts on local systems. To understand this process, it is necessary to identify the systemic and spatial expression of environmental transformation. The objective of this work was to analyze the dynamic adjustment of aboveground net primary production (ANPP) to agricultural intensification between the years 2000 and 2017, using a land use intensity index and the soils’ productive potential. Agricultural expansion and consolidation are observed, as well as the significant intensification throughout the period in 65% of the country’s area—with differences between regions—associated with soil types. ANPP is higher in areas of low land use intensity and lower in high intensity areas, varying from high to low in soils with low to high productive potential, respectively, and growing throughout the period—depending on the area, with less growth in areas of greater intensity. The appropriation of edaphic wealth puts the systemic functionality at risk and challenges these transforming dynamics, with a strong impact on southern systems

Statistical techniques for improving prediction in crop progress stages with meteorological and satellite data

Οι εκθέσεις προόδου της καλλιέργειας (CPR) του USDA παρουσιάζουν την εβδομαδιαία πρόοδο που σημειώθηκε στα διάφορα φαινολογικά στάδια των επιλεγμένων καλλιεργειών και ιδιαίτερα του καλαμποκιού. Σε αυτή την διπλωματική, ο στόχος μας ήταν να προβλέψουμε τα CPR ενός ολόκληρου έτους λαμβάνοντας υπόψη διαθέσιμα δεδομένα από συναφή χαρακτηριστικά με τρόπο που να μπορούμε να νικήσουμε τις προβλέψεις βάσει εμπειρικών μέσων από ιστορικά δεδομένα. Για το λόγο αυτό, χρησιμοποιήσαμε δύο χαρακτηριστικά, τον δείκτη κανονικοποιημένης βλάστησης (NDVI) και τις συγκεντρωτικές ημέρες καλλιέργειας (AGDDs). Προκειμένου να επιτευχθεί ο στόχος μας, εφαρμόσαμε αρκετές προσεγγίσεις μοντελοποίησης, συμπεριλαμβανομένων μοντέλων ανεξάρτητων μήξεων και κρυμμένα μοντέλα HMMs και συγκρίναμε διαφορετικούς τύπους εκτιμητών και προγνωστικών λαμβάνοντας υπόψη και τα δύο χαρακτηριστικά ή τη χωριστή επεξεργασία τους ή πραγματοποιώντας μετασχηματισμούς δεδομένων, όπως διαφορές. Τα αποτελέσματα έδειξαν ότι τα προαναφερθέντα μοντέλα δεν μπορούν να προβλέψουν καλύτερα από τα ιστορικά δεδομένα. Τέλος, κατορθώσαμε να λάβουμε καλύτερες προβλέψεις χρησιμοποιώντας απλή γραμμική παλινδρόμηση. Αυτή η μελέτη μπορεί να επεκταθεί σε διάφορες κατευθύνσεις για μελλοντικές εργασίες.Crop Progress Reports (CPRs) of the USDA are listing the weekly progress made in the different phenological stages of selected crops and in particular of corn. In this thesis, our goal was to predict the CPRs of a full year by taking into account available data from related features in a way that we can beat the predictions based on empirical means from historical data. For this reason, we used two features, the mean Normalized Difference Vegetation Index (NDVI) and the Accumulated Growing Degree Days (AGDDs). In order to achieve our target we implemented several modeling approaches, including Independent Mixture Models and Hidden Markov Models HMMs and we compared different type of estimators and predictors by taking into account both features or treating them separately, or making data transformations, such as differences. The results showed that the aforementioned models cannot predict better than the historical data. Finally, we managed to obtain better predictions by using Simple Linear Regression. This study can be extended in several directions for future work

Quantitative Mapping of Soil Property Based on Laboratory and Airborne Hyperspectral Data Using Machine Learning

Soil visible and near-infrared spectroscopy provides a non-destructive, rapid and low-cost approach to quantify various soil physical and chemical properties based on their reflectance in the spectral range of 400–2500 nm. With an increasing number of large-scale soil spectral libraries established across the world and new space-borne hyperspectral sensors, there is a need to explore methods to extract informative features from reflectance spectra and produce accurate soil spectroscopic models using machine learning. Features generated from regional or large-scale soil spectral data play a key role in the quantitative spectroscopic model for soil properties. The Land Use/Land Cover Area Frame Survey (LUCAS) soil library was used to explore PLS-derived components and fractal features generated from soil spectra in this study. The gradient-boosting method performed well when coupled with extracted features on the estimation of several soil properties. Transfer learning based on convolutional neural networks (CNNs) was proposed to make the model developed from laboratory data transferable for airborne hyperspectral data. The soil clay map was successfully derived using HyMap imagery and the fine-tuned CNN model developed from LUCAS mineral soils, as deep learning has the potential to learn transferable features that generalise from the source domain to target domain. The external environmental factors like the presence of vegetation restrain the application of imaging spectroscopy. The reflectance data can be transformed into a vegetation suppressed domain with a force invariance approach, the performance of which was evaluated in an agricultural area using CASI airborne hyperspectral data. However, the relationship between vegetation and acquired spectra is complicated, and more efforts should put on removing the effects of external factors to make the model transferable from one sensor to another.:Abstract I Kurzfassung III Table of Contents V List of Figures IX List of Tables XIII List of Abbreviations XV 1 Introduction 1 1.1 Motivation 1 1.2 Soil spectra from different platforms 2 1.3 Soil property quantification using spectral data 4 1.4 Feature representation of soil spectra 5 1.5 Objectives 6 1.6 Thesis structure 7 2 Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra 9 2.1 Abstract 10 2.2 Introduction 10 2.3 Materials and methods 13 2.3.1 The LUCAS soil spectral library 13 2.3.2 Partial least squares algorithm 15 2.3.3 Gradient-Boosted Decision Trees 15 2.3.4 Calculation of relative variable importance 16 2.3.5 Assessment 17 2.4 Results 17 2.4.1 Overview of the spectral measurement 17 2.4.2 Results of PLS regression for the estimation of soil properties 19 2.4.3 Results of PLS-GBDT for the estimation of soil properties 21 2.4.4 Relative important variables derived from PLS regression and the gradient-boosting method 24 2.5 Discussion 28 2.5.1 Dimension reduction for high-dimensional soil spectra 28 2.5.2 GBDT for quantitative soil spectroscopic modelling 29 2.6 Conclusions 30 3 Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared Spectroscopy Using Fractal-Based Feature Extraction 31 3.1 Abstract 32 3.2 Introduction 32 3.3 Materials and Methods 35 3.3.1 The LUCAS topsoil dataset 35 3.3.2 Fractal feature extraction method 37 3.3.3 Gradient-boosting regression model 37 3.3.4 Evaluation 41 3.4 Results 42 3.4.1 Fractal features for soil spectroscopy 42 3.4.2 Effects of different step and window size on extracted fractal features 45 3.4.3 Modelling soil properties with fractal features 47 3.4.3 Comparison with PLS regression 49 3.5 Discussion 51 3.5.1 The importance of fractal dimension for soil spectra 51 3.5.2 Modelling soil properties with fractal features 52 3.6 Conclusions 53 4 Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery 55 4.1 Abstract 55 4.2 Introduction 56 4.3 Materials and Methods 59 4.3.1 Datasets 59 4.3.2 Methods 62 4.3.3 Assessment 67 4.4 Results and Discussion 67 4.4.1 Interpretation of mineral and organic soils from LUCAS dataset 67 4.4.2 1D-CNN and spectral index for LUCAS soil clay content estimation 69 4.4.3 Application of transfer learning for soil clay content mapping using the pre-trained 1D-CNN model 72 4.4.4 Comparison between spectral index and transfer learning 74 4.4.5 Large-scale soil spectral library for digital soil mapping at the local scale using hyperspectral imagery 75 4.5 Conclusions 75 5 A Case Study of Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery 77 5.1 Abstract 78 5.2 Introduction 78 5.3 Materials and Methods 81 5.3.1 Study area of Zhangye Oasis 81 5.3.2 Data description 82 5.3.3 Methods 83 5.3.3 Model performance assessment 85 5.4 Results and Discussion 86 5.4.1 The correlation between NDVI and soil salinity 86 5.4.2 Vegetation suppression performance using the Forced Invariance Approach 86 5.4.3 Estimation of soil properties using airborne hyperspectral data 88 5.5 Conclusions 90 6 Conclusions and Outlook 93 Bibliography 97 Acknowledgements 11

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Information retrieval from spaceborne GNSS Reflectometry observations using physics- and learning-based techniques

This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets

Impact of climate change on agricultural and natural ecosystems

This book illustrates the main results deriving from fourteen studies, dealing with the impact of climate change on different agricultural and natural ecosystems, carried out within the Impact of Climate change On agricultural and Natural Ecosystems (ICONE) project funded by the ALFA Programme of the European Commission. During this project, a common methodology on several Global Change-related matters was developed and shared among members of scientific communities coming from Latin America and Europe. In order to facilitate this interdisciplinary approach, specific mobility programmes, addressed to post-graduate, Master and PhD students, have been organized. The research, led by the research groups, was focused on the study of the impact of climate change on various environmental features (i.e. runoff in hydrological basins, soil erosion and moisture, forest canopy, sugarcane crop, land use, drought, precipitation, etc). Integrated and shared methodologies of atmospheric physics, remote sensing, eco-physiology and modelling have been applied

Multiscale Imaging of Evapotranspiration

Evapotranspiration (ET; evaporation + transpiration) is central to a wide range of biological, chemical, and physical processes in the Earth system. Accurate remote sensing of ET is challenging due to the interrelated and generally scale dependent nature of the physical factors which contribute to the process. The evaporation of water from porous media like sands and soils is an important subset of the complete ET problem. Chapter 1 presents a laboratory investigation into this question, examining the effects of grain size and composition on the evolution of drying sands. The effects of composition are found to be 2-5x greater than the effects of grain size, indicating that differences in heating caused by differences in reflectance may dominate hydrologic differences caused by grain size variation. In order to relate the results of Chapter 1 to the satellite image archive, however, the question of information loss between hyperspectral (measurements at 100s of wavelength intervals) laboratory measurements and multispectral (≤ 12 wavelength intervals) satellite images must be addressed. Chapter 2 focuses on this question as applied to substrate materials such as sediment, soil, rock, and non-photosynthetic vegetation. The results indicate that the continuum that is resolved by multispectral sensors is sufficient to resolve the gradient between sand-rich and clay-rich soils, and that this gradient is also a dominant feature in hyperspectral mixing spaces where the actual absorptions can be resolved. Multispectral measurements can be converted to biogeophysically relevant quantities using spectral mixture analysis (SMA). However, retrospective multitemporal analysis first requires cross-sensor calibration of the mixture model. Chapter 3 presents this calibration, allowing multispectral image data to be used interchangeably throughout the Landsat 4-8 archive. In addition, a theoretical explanation is advanced for the observed superior scaling properties of SMA-derived fraction images over spectral indices. The physical quantities estimated by the spectral mixture model are then compared to simultaneously imaged surface temperature, as well as to the derived parameters of ET Fraction and Moisture Availability. SMA-derived vegetation abundance is found to produce substantially more informative ET maps, and SMA-derived substrate fraction is found to yield a surprisingly strong linear relationship with surface temperature. These results provide context for agricultural applications. Chapter 5 investigates the question of mapping and monitoring rice agricultural using optical and thermal satellite image time series. Thermal image time series are found to produce more accurate maps of rice presence/absence, but optical image time series are found to produce more accurate maps of rice crop timing. Chapter 6 takes a more global approach, investigating the spatial structure of agricultural networks for a diverse set of landscapes. Surprisingly consistent scaling relations are found. These relations are assessed in the context of a network-based approach to land cover analysis, with potential implications for the scale dependence of ET estimates. In sum, this thesis present a novel approach to improving ET estimation based on a synthesis of complementary laboratory measurements, satellite image analysis, and field observations. Alone, each of these independent sources of information provides novel insights. Viewed together, these insights form the basis of a more accurate and complete geophysical understanding of the ET phenomenon

Sustainable intensification of arable agriculture:The role of Earth Observation in quantifying the agricultural landscape

By 2050, global food production must increase by 70% to meet the demands of a growing population with shifting food consumption patterns. Sustainable intensification has been suggested as a possible mechanism to meet this demand without significant detrimental impact to the environment. Appropriate monitoring techniques are required to ensure that attempts to sustainably intensify arable agriculture are successful. Current assessments rely on datasets with limited spatial and temporal resolution and coverage such as field data and farm surveys. Earth Observation (EO) data overcome limitations of resolution and coverage, and have the potential to make a significant contribution to sustainable intensification assessments. Despite the variety of established EO-based methods to assess multiple indicators of agricultural intensity (e.g. yield) and environmental quality (e.g. vegetation and ecosystem health), to date no one has attempted to combine these methods to provide an assessment of sustainable intensification. The aim of this thesis, therefore, is to demonstrate the feasibility of using EO to assess the sustainability of agricultural intensification. This is achieved by constructing two novel EO-based indicators of agricultural intensity and environmental quality, namely wheat yield and farmland bird richness. By combining these indicators, a novel performance feature space is created that can be used to assess the relative performance of arable areas. This thesis demonstrates that integrating EO data with in situ data allows assessments of agricultural performance to be made across broad spatial scales unobtainable with field data alone. This feature space can provide an assessment of the relative performance of individual arable areas, providing valuable information to identify best management practices in different areas and inform future management and policy decisions. The demonstration of this agricultural performance assessment method represents an important first step in the creation of an operational EO-based monitoring system to assess sustainable intensification, ensuring we are able to meet future food demands in an environmentally sustainable way

An Integrated Approach for Predicting Nitrogen Status in Early Cotton and Corn

Cotton (Gossypium hirsutum L.) and corn (Zea mays L.) spectral reflectance holds promise for deriving variable rate N (VRN) treatments calibrated with red-edge inflection (REI) type vegetation indices (VIs). The objectives of this study were to define the relationships between two commercially available sensors and the suitable VIs used to predict N status. Field trials were conducted during the 2012-2013 growing seasons using fixed and variable N rates in cotton ranging from 33.6-134.4 kg N ha-1 and fixed N rates in corn ranging from 0.0 to 268.8 kg N ha-1. Leaf N concentration, SPAD chlorophyll and crop yield were analyzed for their relation to fertilizer N treatment. Sensor effects were significant and red-edge VIs most strongly correlated to N status. A theoretical ENDVI index was derived from the research dataset as an improvement and alternative to the Guyot’s Red Edge Inflection and Simplified Canopy Chlorophyll Content Index (SI)