EFFECTS OF ORGANIC FERTILIZERS FOR PASTURE YIELD

The results of a field experiment on the effect of systematic surface application of organic fertilizers on the yield of long-term pastures of herbage are presented. With the introduction of manure in doses of 10 and 20 t/ha (once every 4 years), the yield of the pasture was 3.31 and 3.76 t/ha on average over 45 years, which is 51 and 72% higher compared to unfertilized grass stand.

Plant diversity with species drilled in the same or alternate rows enhanced pasture yield and quality over 4 years

This paper reports on the effects of plant species diversity and sowing method on pasture yield and quality. Nineteen seed mixtures of perennial ryegrass (PR), plantain (Pl), white clover (WC) and red clover (RC) were sown on 26 March 2015 at Lincoln University. Four mixtures of PR, Pl and WC were repeated with species separated in alternate drill rows. Plots were grazed by sheep and irrigated. After 4 years, a mixture with 25% of each species based on seed count – equivalent to 7.5 kg PR, 5.6 kg Pl, 1.9 kg WC and 4.4 kg RC (19.4 kg total seed)/ha – produced an optimal balance of increased total yield (17.44 t DM/ha/yr), weed suppression (0% of total yield), metabolisable energy (11.4 MJ/kg DM) and crude protein (19% of DM). Sowing method had no effect. Plant diversity enhanced pasture production through positive interactions and identity effects among the legumes (WC and RC) and non-legumes (PR and Pl). The strength of interactions between species depended on the identity and relative abundances of the species involved. The diversity effects occurred alongside shifts in species relative abundances over time. This study demonstrated an experimental basis for the evaluation of multi-species pasture mixtures.

Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield

The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. To date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM (tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry provided measures of pasture biomass from many overlapping high-resolution images. These measurements were taken throughout a growing season and were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM (tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.

Determining Antecedent Pasture State for Climate Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite Based Deep-Learning Method for Estimating Time-Integrated Measures of Pasture Yield

The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. Up to date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM(tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry, provided measures of pasture biomass from many overlapping high-resolution images. Repeated remotely piloted aircraft system measures throughout a growing season, were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM(tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.

National Mapping of New Zealand Pasture Productivity Using Temporal Sentinel-2 Data

A national map of pasture productivity, in terms of mass of dry matter yield per unit area and time, enables evaluation of regional and local land-use suitability. Difficulty in measuring this quantity at scale directed this research, which utilises four years of Sentinel-2 satellite imagery and collected pasture yield measurements to develop a model of pasture productivity. The model uses a Normalised Difference Vegetation Index (NDVI), with spatio-temporal segmentation and averaging, to estimate mean annual pasture productivity across all of New Zealand’s grasslands with a standard error of prediction of 2.2 t/ha/y. Regional aggregates of pasture yield demonstrate expected spatial variations. The pasture productivity map may be used to classify grasslands objectively into stratified levels of production on a national scale. Due to its ability to highlight areas of land use intensification suitability, the national map of pasture productivity is of value to landowners, land users, and environmental scientists.

Seventy years of data from the world’s longest grazed and irrigated pasture trials

Pastures are the most widespread land use, globally. The Winchmore trials were established in 1948–1949 in Canterbury, New Zealand and examined either different rates of phosphorus (P) fertiliser on the same irrigation schedule (Fertiliser trial), or different irrigation scheduling at the same rate of P application (Irrigation trial). About 96,000 records of soil chemistry and physical data and pasture yield and botanical composition are available along with nearly 7000 soil samples. These data have been used in 475 publications that have explored topics as diverse as: improvements in sheep, dairy and deer production; the efficacy and scheduling of irrigation; improvements in pasture and crop production; agronomic and environmental soil and water research; and entomology. In addition to above topics, these data are invaluable for calibrating models to predict long-term issues like the accumulation of soil carbon or contaminants like cadmium and informing policy on climate change and agricultural practices. The data and soil samples are available for use and may yet yield discoveries, unforeseen 70 years ago.

Grazing management.

This chapter focuses on grazing management. Topics discussed include: (i) C3 and C4 grasses; (ii) the nutritive value of pastures; (iii) grazing systems; (iv) pasture yield, growth and quality assessment; (v) pasture utilization rate; (vi) calculation of carrying capacities and stocking rates; (vii) pasture budgeting; and (viii) feed-year plans.

Persistence of perennial ryegrass, tall fescue and cocksfoot following sequential annual sowings: pasture yield, composition and density in three establishment years under cattle grazing in Waikato

Persistence is an important component of perennial pasture-grass productivity. Defining the traits that affect persistence is essential for improving pasture longevity through plant breeding and for identifying criteria that should be included in cultivar ranking indices. Compared with a conventional longitudinal study (a single study monitored over time), repeated annual sowings allow the effects on persistence of sowing year and the ensuing interactions between environment and age of pasture to be identified. An experiment commenced in 2016 in Waikato, in which eight cultivars of perennial ryegrass representing different ploidy, flowering date, and cultivar age, and one each of tall fescue and cocksfoot were sown each autumn in a randomised complete block design with four replicates in autumn 2016, spring 2017 and autumn 2018. This paper reports interim data on pasture yield, composition and density in the autumn following each sowing, and for the 2018 sowing only, in the 6 months after sowing. For the three successive autumn measurements there were significant effects due to cultivar, year of sowing and their interactions for all pasture variables. These differences in establishment may have consequences for the future resilience of these pastures under natural biotic and abiotic stressors.