Grower group case study on new farming practices in the Herbert region, Australia

Growers working together have proven to be a successful method for improving the utilization of farm resources and accelerating the adoption of the Sugar Yield Decline Joint Venture principles (SYDJV). The Pinnacle Precision Farming Group was formed in 2004 with the aim to bring together the ideas, knowledge and resources of growers in the Herbert region. Along with their common interest in controlled traffic, minimal tillage and crop rotations, the grower group utilize a farm machinery contractor to provide some of their major farming operations. This paper provides an insight into the changes made by the Pinnacle Precision Farming Group and their journey to adopt the new farming system practices. This paper also details the changes made by the group machinery contractor and a comparison of the old and new farming systems used by a group member. A focus point of the document is the impact of the new farming system on the economic, social and environmental components of the farming business. Analysis of the new farming system with a legume crop rotation revealed an increase in the farm gross margin by AU$22 024 and, in addition, a reduction in tractor operation time by 38% across the whole farm. This represents a return on marginal capital of 14.68 times the original capital outlay required by the group member. Using the new farming system without a legume crop will still improve the group members whole of farm gross margin by AU$6 839 and reduce tractor operation time by 43% across the whole farm. The Pinnacle Precision Farming group recognize the need to continually improve their farming businesses and believe that the new farming system principles are critical for the long term viability of the industry. [U$1 = AU$1.19].

Spatial requirements of animals: Allometry and beyond

Space allowance is a major factor influencing animal welfare. For livestock, at least, it plays a critical role in profitability, yet there is little information on the amount of space that animals require. The amount of space an animal occupies as a consequence of its shape and size can be estimated using allometry; linear dimensions (L) can be expressed as L = kW1/3 and surface area (S) as S = kW2/3, where k = a constant and W = the weight of the animal. Such equations have been used to determine the amount of space needed by standing (area [m2] = 0.019W0.66) and lying (area [m2] = 0.027W0.67) animals. Limited studies on the lying down and standing up behaviors of pigs and cattle suggest that the amount of space required can be estimated by area (m2) = 0.047W0.66. Linear space required per animal for behaviors such as feeding or drinking from a trough can be estimated from 0.064W0.33, but in groups this requirement will be affected by social interactions among group members and the amount of competition for the resource. Determining the amount of space for groups of animals is complex, as the amount of useable space can vary with group size and by how group members share space in time. Some studies have been conducted on the way in which groups of domestic fowl use space, but overall, we know very little about the ways in which livestock time-share space, synchronicity in the performance of behaviors, and the effects of spatial restrictions on behavior and welfare.

An assessment of the genetic relationship between sweet and grain sorghums, within Sorghum bicolor ssp. bicolor (L.) Moench, using AFLP markers

Compared to grain sorghums, sweet sorghums typically have lower grain yield and thick, tall stalks which accumulate high levels of sugar (sucrose, fructose and glucose). Unlike commercial grain sorghum (S. bicolor ssp. bicolor) cultivars, which are usually F1 hybrids, commercial sweet sorghums were selected as wild accessions or have undergone limited plant breeding. Although all sweet sorghums are classified within S. bicolor ssp. bicolor, their genetic relationship with grain sorghums is yet to be investigated. Ninety-five genotypes, including 31 sweet sorghums and 64 grain sorghums, representing all five races within the subspecies bicolor, were screened with 277 polymorphic amplified fragment length polymorphism (AFLP) markers. Cluster analysis separated older sweet sorghum accessions (collected in mid 1800s) from those developed and released during the early to mid 1900s. These groups were emphasised in a principle component analysis of the results such that sweet sorghum lines were largely distinguished from the others, particularly by a group of markers located on sorghum chromosomes SBI-08 and SBI-10. Other studies have shown that QTL and ESTs for sugar-related traits, as well as for height and anthesis, map to SBI-10. Although the clusters obtained did not group clearly on the basis of racial classification, the sweet sorghum lines often cluster with grain sorghums of similar racial origin thus suggesting that sweet sorghum is of polyphyletic origin within S. bicolor ssp. bicolor.

Choosing a fishery's governance structure using data poor methods

Multi-species fisheries are complex to manage and the ability to develop an appropriate governance structure is often seriously impeded because trading between sustainability objectives at the species level, economic objectives at the fleet level, and social objectives at the community scale, is complex. Many of these fisheries also tend to have a mix of information, with stock assessments available for some species and almost no information on other species. The fleets themselves comprise fishers from small family enterprises to large vertically integrated businesses. The Queensland trawl fishery in Australia is used as a case study for this kind of fishery. It has the added complexity that a large part of the fishery is within a World Heritage Area, the Great Barrier Reef Marine Park, which is managed by an agency of the Australian Commonwealth Government whereas the fishery itself is managed by the Queensland State Government. A stakeholder elicitation process was used to develop social, governance, economic and ecological objectives, and then weight the relative importance of these. An expert group was used to develop different governance strawmen (or management strategies) and these were assessed by a group of industry stakeholders and experts using multi-criteria decision analysis techniques against the different objectives. One strawman clearly provided the best overall set of outcomes given the multiple objectives, but was not optimal in terms of every objective, demonstrating that even the "best" strawman may be less than perfect. © 2012.