4 resultados para Non-economic rights

em eResearch Archive - Queensland Department of Agriculture


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Weedy Sporobolus grasses have low palatability for livestock, with infestations reducing land condition and pastoral productivity. Control and containment options are available, but the cost of weed control is high relative to the extra return from livestock, thus, limiting private investment. This paper outlines a process for analysing the economic consequences of alternative management options for weedy Sporobolus grasses. This process is applicable to other weeds and other pastoral degradation or development issues. Using a case study property, three scenarios were developed. Each scenario compared two alternative management options and was analysed using discounted cash flow analysis. Two of the scenarios were based on infested properties and one scenario was based on a currently uninfested property but highly likely to become infested without active containment measures preventing weed seed transport and seedling establishment. The analysis highlighted why particular weedy Sporobolus grass management options may not be financially feasible for the landholder with the infestation. However, at the regional scale, the management options may be highly worthwhile due to a reduction in weed seed movement and new weed invasions. Therefore, to encourage investment by landholders in weedy Sporobolus grass management the investment of public money on behalf of landholders with non-infested properties should be considered.

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In dryland cotton cropping systems, the main weeds and effectiveness of management practices were identified, and the economic impact of weeds was estimated using information collected in a postal and a field survey of Southern Queensland and northern New South Wales. Forty-eight completed questionnaires were returned, and 32 paddocks were monitored in early and late summer for weed species and density. The main problem weeds were bladder ketmia (Hibiscus trionum), common sowthistle (Sonchus oleraceus), barnyard grasses (Echinochloa spp.), liverseed grass (Urochloa panicoides) and black bindweed (Fallopia convolvulus), but the relative importance of these differed with crops, fallows and crop rotations. The weed flora was diverse with 54 genera identified in the field survey. Control of weed growth in rotational crops and fallows depended largely on herbicides, particularly glyphosate in fallow and atrazine in sorghum, although effective control was not consistently achieved. Weed control in dryland cotton involved numerous combinations of selective herbicides, several non-selective herbicides, inter-row cultivation and some manual chipping. Despite this, residual weeds were found at 38-59% of initial densities in about 3-quarters of the survey paddocks. The on-farm financial costs of weeds ranged from $148 to 224/ha.year depending on the rotation, resulting in an estimated annual economic cost of $19.6 million. The approach of managing weed populations across the whole cropping system needs wider adoption to reduce the weed pressure in dryland cotton and the economic impact of weeds in the long term. Strategies that optimise herbicide performance and minimise return of weed seed to the soil are needed. Data from the surveys provide direction for research to improve weed management in this cropping system. The economic framework provides a valuable measure of evaluating likely future returns from technologies or weed management improvements.

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Experiments involving row spacing and tillage, originally established in Mackay and Ingham in 2001, were planted to a second cycle of sugarcane in 2006 following a soybean break. Despite large yield differences, economic analysis indicated that there would be little difference in gross margins because of the much higher costs of the tilled system. It is concluded that without GPS guidance, as was the case with these experiments, cane yields are likely to be reduced with no tillage but these problems may well be overcome by implementing minimum strategic tillage to remove compaction from the planting row.

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The Queensland strawberry (Fragaria ×ananassa) breeding program in subtropical Australia aims to improve sustainable profitability for the producer. Selection must account for the relative economic importance of each trait and the genetic architecture underlying these traits in the breeding population. Our study used estimates of the influence of a trait on production costs and profitability to develop a profitability index (PI) and an economic weight (i.e., change in PI for a unit change in level of trait) for each trait. The economic weights were then combined with the breeding values for 12 plant and fruit traits on over 3000 genotypes that were represented in either the current breeding population or as progenitors in the pedigree of these individuals. The resulting linear combination (i.e., sum of economic weight × breeding value for all 12 traits) estimated the overall economic worth of each genotype as H, the aggregate economic genotype. H values were validated by comparisons among commercial cultivars and were also compared with the estimated gross margins. When the H value of ‘Festival’ was set as zero, the H values of genotypes in the pedigree ranged from –0.36 to +0.28. H was highly correlated (R2 = 0.77) with the year of selection (1945–98). The gross margins were highly linearly related (R2 > 0.98) to H values when the genotype was planted on less than 50% of available area, but the relationship was non-linear [quadratic with a maximum (R2 > 0.96)] when the planted area exceeded 50%. Additionally, with H values above zero, the variation in gross margin increased with increasing H values as the percentage of area planted to a genotype increased. High correlations among some traits allowed the omission of any one of three of the 12 traits with little or no effect on ranking (Spearman’s rank correlation 0.98 or greater). Thus, these traits may be dropped from the aggregate economic genotype, leading to either cost reductions in the breeding program or increased selection intensities for the same resources. H was efficient in identifying economically superior genotypes for breeding and deployment, but because of the non-linear relationship with gross margin, calculation of a gross margin for genotypes with high H is also necessary when cultivars are deployed across more than 50% of the available area.