Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI

This paper explores the use of a novel nonlinear parametric modelling technique based on a Volterra Non-linear Regressive with eXogenous inputs (VNRX) method to quantify the individual, interaction and overall contributions of six soil properties on crop yield and normalised difference vegetation index (NDVI). The proposed technique has been applied on high sampling resolution data of soil total nitrogen (TN) in %, total carbon (TC) in %, potassium (K) in cmol kg−1, pH, phosphorous (P) in mg kg−1 and moisture content (MC) in %, collected with an on-line visible and near infrared (VIS-NIR) spectroscopy sensor from a 18 ha field in Bedfordshire, UK over 2013 (wheat) and 2015 (spring barley) cropping seasons. The on-line soil data were first subjected to a raster analysis to produce a common 5 m by 5 m grid, before they were used as inputs into the VNRX model, whereas crop yield and NDVI represented system outputs. Results revealed that the largest contributions commonly observed for both yield and NDVI were from K, P and TC. The highest sum of the error reduction ratio (SERR) of 48.59% was calculated with the VNRX model for NDVI, which was in line with the highest correlation coefficient (r) of 0.71 found between measured and predicted NDVI. However, on-line measured soil properties led to larger contributions to early measured NDVI than to a late measurement in the growing season. The performance of the VNRX model was better for NDVI than for yield, which was attributed to the exclusion of the influence of crop diseases, appearing at late growing stages. It was recommended to adopt the VNRX method for quantifying the contribution of on-line collected soil properties to crop NDVI and yield. However, it is important for future work to include additional soil properties and to account for other factors affecting crop growth and yield, to improve the performance of the VNRX model

Current data and modeling bottlenecks for predicting crop yields in the United Kingdom

Identifying and implementing management actions that can mitigate the impacts of climate change on domestically grown crops is crucial to maintaining future food security for the United Kingdom (UK). Crop models serve as critical tools for assessing the potential impacts of climate change and making decisions regarding crop management. However, there is often a gap between yields predicted by current modeling methods and observed yields. This has been linked to a sparsity of models that investigate crop yield beyond field scale or that include data on crop management or crop protection factors. It remains unclear whether the lack of available data imposes these limitations or if the currently available data presents untapped opportunities to extend models to better capture the complex ecosystem of factors affecting crop yield. In this paper, we synthesize available data on plant physiology, management, and protection practices for agricultural crops in the UK, as well as associated data on climate and soil conditions. We then compare the available data to the variables used to predict crop yield using current modeling methods. We find there is a lack of openly accessible crop management and crop plant physiology data, particularly for crops other than wheat, which could limit improvements in current crop models. Conversely, data that was found to be available at large scales on climate and soil conditions could be used to explore upscaling of current approaches beyond the field level, and available data on crop protection factors could be integrated into existing models to better account for how disease, insect pest and weed pressures may impact crop yield under different climate scenarios. We conclude that while a lack of available data on crop management, protection, physiology, at scales other than field level, and for species other than wheat currently hampers advancement of modeling methods for UK crops, future investment into data collection and management across a broader range of factors affecting crops, at larger scales and for a broader range of crop species could improve predictions of crop plant development and yield

A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction

An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development

Agroforestry as a post-mining land-use approach for waste deposits in alluvial gold mining areas of Colombia

Alluvial gold mining generates a vast amount of extractive waste that completely covers the natural soil, destroys riparian ecosystems, and negatively impacts river beds and valleys. Since 2002, a gold mining company has striven to create agroforestry plots in the waste deposits as a post-mining management approach, where agricultural crops and livestock are combined to complement reforestation in the area. This research aims at supporting reclamation of waste deposits by providing a comprehensive understanding of processes to manage the transition of nutrient-poor and acidic deposition sites towards productive agroforestry-based systems. Major components of this research comprise (i) an analysis of environmental and social challenges of the gold mining sector in Colombia, and its potential opportunities to add value to affected communities, (ii) an assessment of management practices and decision-making processes of the farmers working on reclamation areas, (iii) an analysis of the sources of variability of waste deposits from the perspective of soil development and vegetation succession, (iv) an analysis of spatial variability of the physicochemical properties of waste deposits with a spatially explicit management scheme, and (v) an assessment of vegetation recovery in terms of biomass and plant community composition. Farmers who are currently working on areas undergoing reclamation rely mostly on their own local knowledge to respond to the challenges that the heavily disturbed conditions of the area pose to crop establishment. Therefore, increasing their awareness of the inherent heterogeneity of their fields, as well as the interdependencies between management practices and improvement of soil fertility, may increase the productivity of their farms. The analysis of sources of variability of the waste deposits generated by alluvial gold mining revealed that these deposits are primarily influenced by the parent material of the alluvial gold deposits and by the technology used for gold mining (bucket or suction dredges), which define the type of deposit formed (gravel or sand). Waste deposits can provide essential functions for rural areas such as woody biomass production and crop establishment if deposits are managed according to a specific purpose, and crop selection for each deposit is done based on physicochemical and structural soil properties. This finding is echoed by the spatial assessment of vegetation reestablishment through the combination of remote sensing with machine-learning techniques that show a high spatial variability of textural properties and nutrient contents of the deposits. A management approach is proposed with the use of delineated management zones, which can lead to an overall increased productivity by developing strategies suitable to the characteristics of each field and its potential uses.Agroforstwirtschaft als Landnutzungsansatz auf Abraumdeponien in alluvialen Goldabbaugebieten Kolumbiens Der Abbau von alluvialem Gold erzeugt eine große Menge mineralischen Abfalls, der den natürlichen Boden vollständig bedeckt, Uferökosysteme zerstört, und Flussbetten und -täler negativ beeinflusst. Von einem Goldminenbetreiber werden seit 2002, als ein Ansatz einer Postbergbaustrategie, Agroforstparzellen in Abraumdeponien angelegt. In diesen werden landwirtschaftliche Nutzpflanzen und Viehhaltung zur Aufforstung der Parzelle kombiniert eingesetzt. Diese Forschungsarbeit beabsichtigt die Rekultivierungsmaßnahmen in Agroforstparzellen durch ein umfassendes Verständnis der beteiligten Prozesse zu unterstützen und den Übergang von nährstoffarmen und sauren Abraumdeponien hin zu produktiven agroforstbasierten Systemen zu steuern. Die Hauptbestandteile dieser Arbeit umfassen (i) eine Analyse der ökologischen und sozialen Herausforderungen des Goldminensektors in Kolumbien und potenzielle Möglichkeiten einen Mehrwert für die betroffenen Gemeinden zu schaffen, (ii) eine Bewertung der Managementpraktiken und Entscheidungsprozesse der Landwirte im Rahmen der Rückgewinnung von Landnutzungsflächen, (iii) eine Analyse der Ursachen von Varianz zwischen Abfalldeponien aus der Perspektive der Boden- und Vegetationsentwicklung, (iv) eine Analyse der räumlichen Variabilität der physikochemischen Eigenschaften von mineralischen Abraumdeponien mit einem räumlich expliziten Managementschema und (v) eine Bewertung der Vegetationserholung im Sinne der Zusammensetzung von Biomasse und Pflanzengemeinschaften. Landwirte die in Gebieten arbeiten die gegenwärtig einer Rekultivierung unterzogen werden, verlassen sich größtenteils auf ihre lokalen Erfahrungswerte, um mit den Herausforderungen für die Nutzpflanzenproduktion umzugehen, die durch die stark gestörten Bodenbedingungen verursacht werden. Eine Steigerung des Bewusstseins der lokalen Farmer für die inhärente Heterogenität ihrer Felder, sowie der Interdependenzen zwischen Managementpraktiken und der Verbesserung der Bodenfruchtbarkeit, kann die Produktivität der Farmbetriebe erhöhen. Die Analyse der Variabilitätsquellen der durch den alluvialen Goldabbau entstandenen mineralischen Abfalllager ergab, dass diese Lagerstätten in erster Linie vom Grundgestein der alluvialen Goldlagerstätten und der verwendeten Abbautechnik (Schaufel- oder Saugbagger) beeinflusst werden. Diese Faktoren bestimmen die Art der gebildeten Ablagerung (Kies oder Sand). Abfalldeponien können wesentliche Funktionen für ländliche Gebiete wie die Produktion von Holzbiomasse und den Anbau von Nutzpflanzen ermöglichen, wenn die Lagerstätten einem bestimmten Zweck entsprechend bewirtschaftet werden und die Auswahl der Kulturen für jede Lagerstätte auf Grundlage der spezifischen physikochemischen und strukturellen Bodeneigenschaften erfolgt. Dieser Befund wird durch die räumliche Bewertung der Vegetationsneubildung durch die Kombination von Fernerkundung mit maschinellen Lerntechniken bestätigt, die eine hohe räumliche Variabilität der Textureigenschaften und Nährstoffgehalte der Deponien zeigt. Es wird ein Managementansatz vorgeschlagen, bei dem abgegrenzte Bewirtschaftungszonen unterteilt werden. Dies kann zu einer insgesamt höheren Produktivität führen, indem Strategien entwickelt werden, die den Eigenschaften jedes einzelnen Feldes und seiner potenziellen Nutzungsmöglichkeiten entsprechen