Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering

Agriculture has undergone some very important changes over the last few decades. The emergence and evolution of precision agri culture has allowed to move from the uniform site management to the site-specific management, with both economic and environmental advan tages. However, to be implemented effectively, site-specific management requires within-field spatial variability to be well-known and character ized. In this paper, an algorithm that delineates within-field management zones in a maize plantation is introduced. The algorithm, based on tri clustering, mines clusters from temporal remote sensing data. Data from maize crops in Alentejo, Portugal, have been used to assess the suit ability of applying triclustering to discover patterns over time, that may eventually help farmers to improve their harvests.Ministerio de Economía y Competitividad TIN2017-88209-C2Fundaçao para a Ciéncia e a Tecnologia (FCT) UIDB/04561/202

On Interpreting Eddy Covariance In Small Area Agricultural Situations With Contrasting Site Management.

This dissertation examined the carbon sequestration potential of a low C:N soil amendment and its incorporation into the soil over a rolling agricultural field. A segmented planar fit was developed to assess and correct the systematic errors the topography introduces on the carbon dioxide fluxes. The carbon dioxide fluxes were then be partitioned into gross primary productivity and soil respiration to understand the influence of the contrasting management practices, using flux variance partitioning. Concomitant with the partitioning, high resolution temporal and spatial scale remote sensing images were interpolated and standardized to conduct hypothesis testing for treatment effects

Learning from Data to Optimize Control in Precision Farming

Precision farming is one way of many to meet a 70 percent increase in global demand for agricultural products on current agricultural land by 2050 at reduced need of fertilizers and efficient use of water resources. The catalyst for the emergence of precision farming has been satellite positioning and navigation followed by Internet-of-Things, generating vast information that can be used to optimize farming processes in real-time. Statistical tools from data mining, predictive modeling, and machine learning analyze pattern in historical data, to make predictions about future events as well as intelligent actions. This special issue presents the latest development in statistical inference, machine learning and optimum control for precision farming.Comment: Editorial of "Statistical Tools in Precision Farming", MDPI/Stat

Development of phenomic-assisted breeding methodologies for prescriptive plant breeding, efficient cultivar testing, and genomic studies

Plant scientists are beginning to harness the capabilities of high dimensional ‘omic tools (e.g., genomic, phenomic) to usher in the era of digital agriculture to allow the usage of predictive analytics. While genomic tools have been developed to exploit high-density genetic markers for breeding decision making, a gap persists in the availability of phenomic-assisted breeding methodologies. Here we develop frameworks malleable to crop species and breeding objective to leverage complex high-dimension phenomic data using machine learning (ML) and optimization techniques for the development of data driven solutions designed to empower plant scientists to; develop prescriptive breeding solutions, improve the operation efficiency of breeding programs, and to expand the capacity of current phenotyping efforts through the use of a fine-tuned package of sensors assembled for a specific breeding objective. In this consortium of work, we show that phenomic predictors can be deployed for ML assisted prescriptive-breeding techniques for precision product placement and in turn these same phenomic predictors can be used for efficient cultivar testing (e.g., seed yield) to optimize breeding program operational efficiencies. Furthermore, phenomic sensors provided a wealth of data making this work ripe for genomic studies revealing the underlying genomic regions controlling yield predicting phenomic traits and rapid scanning of genotyped germplasm using genomic prediction. This work will allow breeders to continually optimize their breeding programs to begin fusing widely available genomic data with the upcoming capabilities of high throughput phenotyping techniques to streamline cultivar development pipelines

Using hydrological models and digital soil mapping for the assessment and management of catchments: A case study of the Nyangores and Ruiru catchments in Kenya (East Africa)

Human activities on land have a direct and cumulative impact on water and other natural resources within a catchment. This land-use change can have hydrological consequences on the local and regional scales. Sound catchment assessment is not only critical to understanding processes and functions but also important in identifying priority management areas. The overarching goal of this doctoral thesis was to design a methodological framework for catchment assessment (dependent upon data availability) and propose practical catchment management strategies for sustainable water resources management. The Nyangores and Ruiru reservoir catchments located in Kenya, East Africa were used as case studies. A properly calibrated Soil and Water Assessment Tool (SWAT) hydrologic model coupled with a generic land-use optimization tool (Constrained Multi-Objective Optimization of Land-use Allocation-CoMOLA) was applied to identify and quantify functional trade-offs between environmental sustainability and food production in the ‘data-available’ Nyangores catchment. This was determined using a four-dimension objective function defined as (i) minimizing sediment load, (ii) maximizing stream low flow and (iii and iv) maximizing the crop yields of maize and soybeans, respectively. Additionally, three different optimization scenarios, represented as i.) agroforestry (Scenario 1), ii.) agroforestry + conservation agriculture (Scenario 2) and iii.) conservation agriculture (Scenario 3), were compared. For the data-scarce Ruiru reservoir catchment, alternative methods using digital soil mapping of soil erosion proxies (aggregate stability using Mean Weight Diameter) and spatial-temporal soil loss analysis using empirical models (the Revised Universal Soil Loss Equation-RUSLE) were used. The lack of adequate data necessitated a data-collection phase which implemented the conditional Latin Hypercube Sampling. This sampling technique reduced the need for intensive soil sampling while still capturing spatial variability. The results revealed that for the Nyangores catchment, adoption of both agroforestry and conservation agriculture (Scenario 2) led to the smallest trade-off amongst the different objectives i.e. a 3.6% change in forests combined with 35% change in conservation agriculture resulted in the largest reduction in sediment loads (78%), increased low flow (+14%) and only slightly decreased crop yields (3.8% for both maize and soybeans). Therefore, the advanced use of hydrologic models with optimization tools allows for the simultaneous assessment of different outputs/objectives and is ideal for areas with adequate data to properly calibrate the model. For the Ruiru reservoir catchment, digital soil mapping (DSM) of aggregate stability revealed that susceptibility to erosion exists for cropland (food crops), tea and roadsides, which are mainly located in the eastern part of the catchment, as well as deforested areas on the western side. This validated that with limited soil samples and the use of computing power, machine learning and freely available covariates, DSM can effectively be applied in data-scarce areas. Moreover, uncertainty in the predictions can be incorporated using prediction intervals. The spatial-temporal analysis exhibited that bare land (which has the lowest areal proportion) was the largest contributor to erosion. Two peak soil loss periods corresponding to the two rainy periods of March–May and October–December were identified. Thus, yearly soil erosion risk maps misrepresent the true dimensions of soil loss with averages disguising areas of low and high potential. Also, a small portion of the catchment can be responsible for a large proportion of the total erosion. For both catchments, agroforestry (combining both the use of trees and conservation farming) is the most feasible catchment management strategy (CMS) for solving the major water quantity and quality problems. Finally, the key to thriving catchments aiming at both sustainability and resilience requires urgent collaborative action by all stakeholders. The necessary stakeholders in both Nyangores and Ruiru reservoir catchments must be involved in catchment assessment in order to identify the catchment problems, mitigation strategies/roles and responsibilities while keeping in mind that some risks need to be shared and negotiated, but so will the benefits.:TABLE OF CONTENTS DECLARATION OF CONFORMITY........................................................................ i DECLARATION OF INDEPENDENT WORK AND CONSENT ............................. ii LIST OF PAPERS ................................................................................................. iii ACKNOWLEDGEMENTS ..................................................................................... iv THESIS AT A GLANCE ......................................................................................... v SUMMARY ............................................................................................................ vi List of Figures......................................................................................................... x List of Tables........................................................................................................... x ABBREVIATION..................................................................................................... xi PART A: SYNTHESIS 1. INTRODUCTION ............................................................................................... 1 1.1 Catchment management ...................................................................................1 1.2 Tools to support catchment assessment and management ..............................4 1.3 Catchment management strategies (CMSs)......................................................9 1.4 Concept and research objectives.......................................................................11 2. MATERIAL AND METHODS................................................................................15 2.1. STUDY AREA ..................................................................................................15 2.1.1. Nyangores catchment ...................................................................................15 2.1.2. Ruiru reservoir catchment .............................................................................17 2.2. Using SWAT conceptual model and land-use optimization ..............................19 2.3. Using soil erosion proxies and empirical models ..............................................21 3. RESULTS AND DISCUSSION..............................................................................24 3.1. Assessing multi-metric calibration performance using the SWAT model...........25 3.2. Land-use optimization using SWAT-CoMOLA for the Nyangores catchment. ..26 3.3. Digital soil mapping of soil aggregate stability ..................................................28 3.4. Spatio-temporal analysis using the revised universal soil loss equation (RUSLE) 29 4. CRITICAL ASSESSMENT OF THE METHODS USED ......................................31 4.1. Assessing suitability of data for modelling and overcoming data challenges...31 4.2. Selecting catchment management strategies based on catchment assessment . 35 5. CONCLUSION AND RECOMMENDATIONS ....................................................36 6. REFERENCES ............................ .....................................................................38 PART B: PAPERS PAPER I .................................................................................................................47 PAPER II ................................................................................................................59 PAPER III ...............................................................................................................74 PAPER IV ...............................................................................................................8