A simple statistical model for predicting herbage production from permanent grassland

Abstract

The considerable year-to-year and seasonal variation in grassland production is of major importance to dairy farmers in Europe, as production systems must allow for the risk of unfavourable weather conditions. A large portion of the variability is caused by weather and its interaction with soil conditions and grassland management. The present study takes advantage of the interactions between weather, soil conditions and grassland management to derive a reliable grassland statistical model (GRAM) for grasslands under various management regimes using polynomial regressions (GRAM-R) and neural networks (GRAM-N). The model performance was tested with a focus on predicting its capability during unusually dry or wet years using long-term experimental data from Austrian sites. The GRAM model was then coupled with the Met&Roll stochastic weather generator to provide estimates of harvestable herbage dry matter (DM) production early in the season. It was found that, with the GRAM-N or GRAM-R methodology, up to 0AE78 of the variability in harvested herbage DM production could be explained with a systematic bias of 1AE1-2AE3%. The models showed stable performance over subsets of dry and wet years. Generalized GRAM models were also successfully used to estimate daily herbage growth during the season, explaining between 0AE63 and 0AE91 of variability in individual cases. It was possible to issue a probabilistic forecast of the harvestable herbage DM production early in the season with reasonable accuracy. The overall results showed that the GRAM model could be used instead of (or in parallel with) more sophisticated grassland models in areas or sites where complete data sets are not yet available. As the model was tested under various climatic and soil conditions, it is suggested that the proposed approach could be used for comparable temperate grassland sites throughout Europe.

Proxy quantification and mapping of seasonal rangeland herbage using grass cover in the cattle corridor of Uganda

“…The determination coefficients were higher than what was reported in related studies for rangelands in Asia by Suzuki et al (2012) and Li Jianlong and Quangong (1998). In Australia, an estimation model by Trnka et al (2006) explained 78% variability in harvested herbage DM production. Results from stepwise regression analysis with harvested DM yield indicated that grass cover measurements alone could explain 73% of herbage DM yield.…”

Inter-paddock annual dry matter yield variability for dairy farms in the Waikato region of New Zealand

“…There is a paucity of published work with which to compare these results. Trnka et al (2006) compiled long-term (20 years) annualised pasture (predominantly perennial ryegrass) yield from three sites in Austria. Mean annual DM yield for pasture cut three times through the season with approximately 30 kg N applied after each cut was 9.4, 8.2 and 7.3 t/ha with minimum and maximum annual DM production in the range 7.4Á12.3, 4.7Á12.5 and 5.9Á8.8 t/ha, respectively for the three sites, equating to a difference of 49Á 165%, similar to the worst and best producing individual paddocks in the current study.…”

A Review of Planetary Boundary Layer Parameterization Schemes and Their Sensitivity in Simulating Southeastern U.S. Cold Season Severe Weather Environments

“…While this framework is often applied in economic forecast analysis (e.g., Theil 1961Theil , 1966Clements and Frenkel 1980;Pindyck and Rubinfeld 1981), its statistical applicability is broader among other fields (Trnka et al 2006). Theil's inequality coefficient is defined as…”