Effects of vermicomposted spent mushroom compost on growing medium characteristics, plant growth, yield and abiotic stress tolerance

Treatment of agricultural biodegradable wastes and by-products can be carried out using composting or vermicomposting, or a combination of both treatment methods, to create a growing medium amendment suitable for horticultural use. When compared to traditional compost-maturation, vermicompost-maturation resulted in a more mature growing medium amendment i.e. lower C/N and pH, with increased nutrient content and improved plant growth response, increasing lettuce shoot fresh and dry weight by an average of 15% and 14%, respectively. Vermicomposted horse manure compost was used as a growing medium amendment for lettuce and was found to significantly increase lettuce shoot and root growth, and chlorophyll content. When used as a growing medium amendment for tomato fruit production, vermicomposted spent mushroom compost increased shoot growth and marketable yield, and reduced blossom end rot in two independent studies. Vermicompost addition to peat-based growing media increased marketable yield by an average of 21%. Vermicompost also improved tomato fruit quality parameters such as acidity and sweetness. Fruit sweetness, as measured using Brix value, was significantly increased in fruits grown with 10% or 20% vermicompost addition by 0.2 in truss one and 0.3 in truss two. Fruit acidity (% citric acid) was significantly increased in plants grown with vermicompost by an average of 0.65% in truss one and 0.68% in truss two. These changes in fruit chemical parameters resulted in a higher tomato fruit overall acceptability rating as determined by a consumer acceptance panel. When incorporated into soil, vermicomposted spent mushroom compost increased plant growth and reduced plant stress under conditions of cold stress, but not salinity or heat stress. The addition of 20% vermicompost to cold-stressed plants increased plant growth by an average of 30% and increased chlorophyll fluorescence by an average of 21%. Compared to peat-based growing medium, vermicompost had consistently higher nutrient content, pH, electrical conductivity and bulk density, and when added to a peat-based growing medium, vermicomposted spent mushroom compost altered the microbial community. Vermicompost amendment increased the microbial activity of the growing medium when incorporated initially, and this increased microbial activity was observed for up to four months after incorporation when plants were grown in it. Vermicomposting was shown to be a suitable treatment method for agricultural biodegradable wastes and by-products, with the resulting vermicompost having suitable physical, chemical and biological properties, and resulting in increased plant growth, marketable yield and yield quality, when used as an amendment in peat-based growing medium.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.