Reduced stem length increases perennial ryegrass seed yield

The effect of plant growth regulators inducing severe stem shortening on the seed production of perennial ryegrass (Lolium perenne) was investigated over two seasons in Canterbury using the tretraploid, late season cultivars ‘Halo’ (2012-13) and ‘Bealey’ (2013-14). Stem shortening was achieved through the application of Moddus® (active ingredient 250 g/l trinexapac-ethyl, TE) either alone or in combination with Payback™ (active ingredient 250 g/l paclobutrazol, PB) and ‘Cycocel®’ (active ingredient 750 g/l chlormequat-chloride, CCC). Trinexapac ethyl applied as a single application at Zadoks growth stage 32 increased seed yield by up to 44% as rates increased from the untreated (1720 kg/ha) to 3.2 l/ha (2470 kg/ha). Where TE was applied in sequences (Zadoks growth stages 30, 32 and 39) seed yield was increased by 59% to 2730 kg/ha. Combinations of TE, PB and CCC increased seed yields by up to 95% (3360 kg/ha) above the untreated control. Seed yield increase was achieved through an increased number of seeds/m². Total stem length was reduced from 105 cm to 85 cm by applications of TE alone and further to 65 cm where applications of TE, PB and CCC were applied in combination. On average, each centimetre of stem length reduction increased seed yield by 45 kg/ha. Stem length reduction was associated with delayed onset of lodging and absolute lodging at harvest, where only crops shorter than 71 cm remained standing at harvest. These results suggest that growers should aim to shorten ryegrass stem lengths to approximately 70 cm to reduce lodging and maximise seed yields

Using LIDAR to measure forage yield of perennial ryegrass (Lolium perenne L.) field plots

A LIDAR-based tool for non-invasive estimation of plant biomass in perennial ryegrass field plots was developed. This included designing and making a prototype of a machine for LIDAR data collection, and developing algorithms for data processing. The biomass estimates were validated with regression analysis against harvest data. The project was implemented in three phases. In phase 1, a prototype carrying frame and a light-excluding cover was constructed for the LIDAR scanner. An algorithm was developed for grass plot segmentation, ground surface detection and estimation of plant biomass. Phase 2 focused on developing the prototype tool further, including application-specific real-time capture end-user software for data capture and analysis. This included testing the algorithm and in-field testing of the software. An experiment was also conducted to study how the variation in ground level between different scans affected the measurement. It was found that the variation of ground level was significant (more than 20 mm) between adjacent scans and within each segment. An improved method with correction for soil surface variation was developed to estimate the ground level of each scan and increase the accuracy of biomass estimation. In phase 3, 86 segments in replicated field plots of a perennial ryegrass cultivar trial in Canterbury, New Zealand, were scanned with LIDAR at early, mid and late time points, with mechanical harvest and yield data collection at the late growth stage. Significant (P<0.0005) correlations were observed between processed LIDAR data and fresh and dry weights of plant foliage biomass with R2 values of 0.78 and 0.76, respectively. The late-growth calibrated data were used to explore ryegrass growth dynamics using LIDAR scans at early growth and mid-growth stages