Managing for rainfall variability: effect of grazing strategy on cattle production in a dry tropical savanna

Rainfall variability is a challenge to sustainable and pro. table cattle production in northern Australia. Strategies recommended to manage for rainfall variability, like light or variable stocking, are not widely adopted. This is due partly to the perception that sustainability and profitability are incompatible. A large, long-term grazing trial was initiated in 1997 in north Queensland, Australia, to test the effect of different grazing strategies on cattle production. These strategies are: (i) constant light stocking (LSR) at long-term carrying capacity (LTCC); (ii) constant heavy stocking (HSR) at twice LTCC; (iii) rotational wet-season spelling (R/Spell) at 1.5 LTCC; (iv) variable stocking (VAR), with stocking rates adjusted in May based on available pasture; and (v) a Southern Oscillation Index (SOI) variable strategy, with stocking rates adjusted in November, based on available pasture and SOI seasonal forecasts. Animal performance varied markedly over the 10 years for which data is presented, due to pronounced differences in rainfall and pasture availability. Nonetheless, lighter stocking at or about LTCC consistently gave the best individual liveweight gain (LWG), condition score and skeletal growth; mean LWG per annum was thus highest in the LSR (113 kg), intermediate in the R/Spell (104 kg) and lowest in the HSR(86 kg). MeanLWGwas 106 kg in the VAR and 103 kg in the SOI but, in all years, the relative performance of these strategies was dependent upon the stocking rate applied. After 2 years on the trial, steers from lightly stocked strategies were 60-100 kg heavier and received appreciable carcass price premiums at the meatworks compared to those under heavy stocking. In contrast, LWG per unit area was greatest at stocking rates of about twice LTCC; mean LWG/ha was thus greatest in the HSR (21 kg/ha), but this strategy required drought feeding in four of the 10 years and was unsustainable. Although LWG/ha was lower in the LSR (mean 14 kg/ha), or in strategies that reduced stocking rates in dry years like the VAR(mean 18 kg/ha) and SOI (mean 17 kg/ha), these strategies did not require drought feeding and appeared sustainable. The R/Spell strategy (mean 16 kg/ha) was compromised by an ill-timed fire, but also performed satisfactorily. The present results provide important evidence challenging the assumption that sustainable management in a variable environment is unprofitable. Further research is required to fully quantify the long-term effects of these strategies on land condition and profitability and to extrapolate the results to breeder performance at the property level.

The effect of data sources and quality on the predictive capacity of CLIMEX models: an assessment of Teleonemia scrupulosa and Octotoma scabripennis for the biocontrol of Lantana camara in Australia

Understanding the effects of different types and quality of data on bioclimatic modeling predictions is vital to ascertaining the value of existing models, and to improving future models. Bioclimatic models were constructed using the CLIMEX program, using different data types – seasonal dynamics, geographic (overseas) distribution, and a combination of the two – for two biological control agents for the major weed Lantana camara L. in Australia. The models for one agent, Teleonemia scrupulosa Stål (Hemiptera:Tingidae) were based on a higher quality and quantity of data than the models for the other agent, Octotoma scabripennis Guérin-Méneville (Coleoptera: Chrysomelidae). Predictions of the geographic distribution for Australia showed that T. scrupulosa models exhibited greater accuracy with a progressive improvement from seasonal dynamics data, to the model based on overseas distribution, and finally the model combining the two data types. In contrast, O. scabripennis models were of low accuracy, and showed no clear trends across the various model types. These case studies demonstrate the importance of high quality data for developing models, and of supplementing distributional data with species seasonal dynamics data wherever possible. Seasonal dynamics data allows the modeller to focus on the species response to climatic trends, while distributional data enables easier fitting of stress parameters by restricting the species envelope to the described distribution. It is apparent that CLIMEX models based on low quality seasonal dynamics data, together with a small quantity of distributional data, are of minimal value in predicting the spatial extent of species distribution.