Lipy Adhikari , Peter de Voil , M. Fernanda Dreccer , Daniel Rodriguez
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引用次数: 0
Abstract
CONTEXT
In Northern Queensland, farmers have limited information on options to increase resilience to climate variability and market volatility by diversifying production activities in large-scale rangeland grazing farms.
OBJECTIVE
To quantify benefits and trade-offs of co-designed diversification options of large-scale rangeland grazing farms in Northern Queensland.
METHODS
A diversification framework, specifically designed for large-scale farms (Adhikari et al., 2023), guided a participatory modelling exercise to co-design alternative production options. Two representative case-study farms, a dryland cropping (DC) and a dryland rangeland (DR), were selected from Northern Queensland's Gilbert region. Discussions with farmers were used to parameterise the APSIM model and simulate farmer's proposed alternative production systems. For DC, the baseline scenario was the existing dryland cotton-maize rotation, and the farmer's interest was to diversify into irrigated cotton, legume, and winter cereal rotations. For DR, the baseline scenario was growing dryland forages, and the diversification options included a dryland cotton-mungbean crop rotation. Benefits and trade-offs were quantified on economic, risk, environmental and social indicators.
RESULTS AND CONCLUSIONS
Irrigating legumes, and winter crops, alongside the existing cotton-maize rotation, increased the mean gross margin by ∼$1500 ha−1 year−1 and reduced downside risks by ∼25 % in the DC. Under irrigation, adding double-cropped winter-maize in rotation with cotton tripled ground cover and reduced erosion losses by ∼6 t ha−1 year−1. However, this was at the expense of a lower water-use efficiency ($ML−1), which was undesirable to the farmer. For the DR farm, the proposed dryland cropping increased downside risk by >65 % over the existing forage system, and reduced erosion by ∼3 t ha−1 year−1 reinforcing farmers' preference to maintain the current forage production system. In both cases, “peer-learning” emerged as the preferred method for upskilling as it does not necessitate substantial investment, is based on trust, and yet promises considerable positive impact in the farming business.
SIGNIFICANCE
Our results show that diversification doesn't always ensure positive outcomes. By co-designing options and quantifying benefits and trade-offs across multiple dimensions, discussions between researchers and farmers were better informed, allowing for improved decision making. Additionally, aligning diversification options with the local context and farmers' needs improves the likelihood of adoption and facilitates the scaling-up of these efforts. A major constraint includes inherent complexities of the participatory modelling approach that require skilling up of regional development agencies to actively engage and assist stakeholders for the agricultural development of Northern Australia.
期刊介绍:
Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments.
The scope includes the development and application of systems analysis methodologies in the following areas:
Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making;
The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment;
Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems;
Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.