Unveiling the potential of proximal hyperspectral sensing for measuring herbage nutritive value in a pasture-based dairy farm system : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agriculture and Horticulture at Massey University, Manawatū, New Zealand

The aim of this thesis was to unveil the potential of proximal hyperspectral sensing for measuring herbage nutritive value in a pasture based-dairy farm system. Hyperspectral canopy reflectance and herbage cuts as well as data on herbage and supplement allocation, and milk production were collected regularly from Dairy 1 farm at Massey University during the 2016-17 and 2017-18 production seasons. Milk, fat and protein yields and body condition score of cows were measured at monthly herd tests while live weights were recorded daily. Calibration equations determining herbage the nutritive value traits digestible organic matter in dry matter, metabolisable energy (ME), crude protein, neutral detergent fibre and acid detergent fibre from hyperspectral canopy reflectance data were developed and validated using partial least squares regression. Canopy reflectance calibration models were able to determine the various herbage nutritive value traits with R2 values ranging from 0.57 to 0.78. Variation of herbage nutritive value traits were mostly explained by month within production season (42.7% of variance among traits) followed by random error (33.4%), production season (13.1%) and paddock (10.7%). The relative importance of herbage nutritive value and other herbage quantity and climate-related variables in driving performance per cow in the herd was determined using multiple linear regression. Herbage metabolizable energy explained 20% to 30% of milk, fat and protein production per cow while herbage quantity and climate- related factors were relatively less important (below 15%). Random regression models were used to model lactation curves of milk, fat, protein and live weight to estimate daily ME requirements of individual cows. The daily ME estimated requirements was nearly a fifth above or below the daily mean ME supplied. The deviation of the daily ME estimated requirements of a cow from the actual ME supplied per cow in the herd was mostly explained by the observations made within a cow rather than between cows or breeds. Variation in herbage nutritive value in addition to the within and between cow variation of ME estimated requirements were high enough to justify the use of proximal hyperspectral sensing as measurement tool to assist with feed allocation decision-making. However, the potential of this technology could be further enhanced using more precise technologies to allocate herbage to individual cows or groups of cows. The potential benefits of more precise feed allocation will result in more efficient grazing management and thus improved utilisation of herbage and hence milk production

Fire

Vegetation plays a crucial role in regulating environmental conditions, including weather and climate. The amount of water and carbon dioxide in the air and the albedo of our planet are all influenced by vegetation, which in turn influences all life on Earth. Soil properties are also strongly influenced by vegetation, through biogeochemical cycles and feedback loops (see Volume 1A—Section 4). Vegetated landscapes on Earth provide habitat and energy for a rich diversity of animal species, including humans. Vegetation is also a major component of the world economy, through the global production of food, fibre, fuel, medicine, and other plantbased resources for human consumptio

Drones and Geographical Information Technologies in Agroecology and Organic Farming

Although organic farming and agroecology are normally not associated with the use of new technologies, it’s rapid growth, new technologies are being adopted to mitigate environmental impacts of intensive production implemented with external material and energy inputs. GPS, satellite images, GIS, drones, help conventional farming in precision supply of water, pesticides, fertilizers. Prescription maps define the right place and moment for interventions of machinery fleets. Yield goal remains the key objective, integrating a more efficient use or resources toward an economic-environmental sustainability. Technological smart farming allows extractive agriculture entering the sustainability era. Societies that practice agroecology through the development of human-environmental co-evolutionary systems represent a solid model of sustainability. These systems are characterized by high-quality agroecosystems and landscapes, social inclusion, and viable economies. This book explores the challenges posed by the new geographic information technologies in agroecology and organic farming. It discusses the differences among technology-laden conventional farming systems and the role of technologies in strengthening the potential of agroecology. The first part reviews the new tools offered by geographic information technologies to farmers and people. The second part provides case studies of most promising application of technologies in organic farming and agroecology: the diffusion of hyperspectral imagery, the role of positioning systems, the integration of drones with satellite imagery. The third part of the book, explores the role of agroecology using a multiscale approach from the farm to the landscape level. This section explores the potential of Geodesign in promoting alliances between farmers and people, and strengthening food networks, whether through proximity urban farming or asserting land rights in remote areas in the spirit of agroecological transition. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons 4.0 license

Mapping the spatial and temporal stability of production in mixed farming systems: an index that integrates crop and pasture productivity to assist in the management of variability

While precision agriculture (PA) technologies are widely used in cropping systems, these technologies have received less attention in mixed farming systems. Little is known about the nature, extent, and temporal stability of spatial variability of pastures in mixed farming systems and the feasibility of managing this variability. This paper describes a technique to create a Stability Index based on both crop grain yield and pasture total green dry matter (TGDM) production over time, using high resolution spatial data in two climatic zones of Australia. Four productivity zones were used to characterise the Stability Index: high and stable, high and unstable, low and stable, and low and unstable. Mapping the indices shows the location and size of the spatial and temporal features of each paddock. The features of the stability zones generally corresponded with soil texture classes. Testing the Stability Indices with a Kruskal–Wallis one-way ANOVA showed significantly different medians for high and low production categories for both grain yield and pasture TGDM (p < 0.01). Crop grain yield stability showed significant differences between medians. In pasture TGDM, the differences between stability medians were not significant, but the technique still separated medians into stable and unstable groupings. This production Stability Index has the potential to be used by farmers to manage spatial variability in mixed farming systems by identifying homogenous areas within a paddock for investigation/amelioration and can also separate out areas of either spatial and/or temporal instability for specific management strategies

The application of Earth Observation for mapping soil saturation and the extent and distribution of artificial drainage on Irish farms

Artificial drainage is required to make wet soils productive for farming. However, drainage may have unintended environmental consequences, for example, through increased nutrient loss to surface waters or increased flood risk. It can also have implications for greenhouse gas emissions. Accurate data on soil drainage properties could help mitigate the impact of these consequences. Unfortunately, few countries maintain detailed inventories of artificially-drained areas because of the costs involved in compiling such data. This is further confounded by often inadequate knowledge of drain location and function at farm level. Increasingly, Earth Observation (EO) data is being used map drained areas and detect buried drains. The current study is the first harmonised effort to map the location and extent of artificially-drained soils in Ireland using a suite of EO data and geocomputational techniques. To map artificially-drained areas, support vector machine (SVM) and random forest (RF) machine learning image classifications were implemented using Landsat 8 multispectral imagery and topographical data. The RF classifier achieved overall accuracy of 91% in a binary segmentation of artifically-drained and poorly-drained classes. Compared with an existing soil drainage map, the RF model indicated that ~44% of soils in the study area could be classed as “drained”. As well as spatial differences, temporal changes in drainage status where detected within a 3 hectare field, where drains installed in 2014 had an effect on grass production. Using the RF model, the area of this field identified as “drained” increased from a low of 25% in 2011 to 68% in 2016. Landsat 8 vegetation indices were also successfully applied to monitoring the recovery of pasture following extreme saturation (flooding). In conjunction with this, additional EO techniques using unmanned aerial systems (UAS) were tested to map overland flow and detect buried drains. A performance assessment of UAS structure-from-motion (SfM) photogrammetry and aerial LiDAR was undertaken for modelling surface runoff (and associated nutrient loss). Overland flow models were created using the SIMWE model in GRASS GIS. Results indicated no statistical difference between models at 1, 2 & 5 m spatial resolution (p< 0.0001). Grass height was identified as an important source of error. Thermal imagery from a UAS was used to identify the locations of artifically drained areas. Using morning and afternoon images to map thermal extrema, significant differences in the rate of heating were identified between drained and undrained locations. Locations of tiled and piped drains were identified with 59 and 64% accuracy within the study area. Together these methods could enable better management of field drainage on farms, identifying drained areas, as well as the need for maintenance or replacement. They can also assess whether treatments have worked as expected or whether the underlying saturation problems continues. Through the methods developed and described herein, better characterisation of drainage status at field level may be achievable

Understanding the spatiotemporal heterogeneity in grassland dynamics in Kenya’s semi-arid pastures

Grassland biomes are one of the largest terrestrial ecosystems on the planet, providing critical ecological, social, and economic benefits. However, they are subjected to natural and anthropogenic stresses such as precipitation, temperature variability, and widespread land degradation. Invasive plant species, for example, pose enormous challenges regarding biodiversity loss and degradation. Therefore, we need to keep up with and improve our knowledge of how they change, especially over space and time, to make good decisions about their productivity, management, and conservation. However, questions remain in (i) mapping and invasion science regarding a methodological framework for mapping invasive plant species (Opuntia stricta) using satellite remote sensing, (ii) understanding the spatiotemporal relationship between grassland greenness, communities, precipitation, temperature, and grazing factors, and (iii) our understanding of the spatial variation in grassland community types and their palatability probability. As a result, this study aimed to better understand the spatiotemporal dynamics in Kenya’s heterogeneous semi-arid grasslands by characterising the grassland into grassland communities and palatable and non-palatable plants. Additionally, it evaluates the intra-seasonal drivers of grassland changes at a site-specific level in 2019. The results show that combining Sentinel-2 spectral data, vegetation, and topographic indices is sufficient to map Opuntia stricta in a complex, heterogeneous semi-arid landscape. Additionally, precipitation, temperature and grazing, though at different times, are the major drivers of intra-seasonal grassland dynamics in semi-arid areas. Furthermore, the study found Sentinel-2 imagery to be adequate in achieving fine-scale spatial variations in grassland communities and inferring palatability probability in heterogeneous semi-arid grasslands. Finally, these findings and recommendations can help us better understand grassland dynamics and uncertainty modelling, as well as improve our understanding of plant-animal interactions, which can lead to management implications for rangeland management in terms of productivity, conservation, and rehabilitation