Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Simulating Demography, Monitoring, and Management Decisions to Evaluate Adaptive Management Strategies for Endangered Species(Wiley, 2025-04-02) Canessa S; Converse SJ; Adams L; Armstrong DP; Makan T; McCready M; Parker KA; Parlato EH; Sipe HA; Ewen JGAdaptive management (AM) remains underused in conservation, partly because optimization-based approaches require real-world problems to be substantially simplified. We present an approach to AM based in management strategy evaluation, a method used largely in fisheries. Managers define objectives and nominate alternative adaptive strategies, whose future performance is simulated by integrating ecological, learning and decision processes. We applied this approach to conservation of hihi (Notiomystis cincta) across Aotearoa-New Zealand. For multiple extant and prospective hihi populations, we jointly simulated demographic trends, monitoring, estimation, and decisions including translocations and supplementary feeding. Results confirmed that food supplementation assisted recovery, but was more intensive and expensive. Over 20 years, actively pursuing learning, for example by removing food from populations, provided little benefit. Recovery group members supported continuing current management or increasing priority on existing populations before reintroducing new populations. Our simulation-based approach can complement formal optimization-based approaches and improve AM uptake, particularly for programs involving many complex and coordinated decisions.Item Reduced anthelmintic use on 13 New Zealand sheep farms: farmer motivations and practical implementation(Taylor and Francis Group on behalf of the New Zealand Veterinary Association, 2024-09-19) Ridler AL; Hytten K; Gray DI; Reid JIAIMS: To describe the personal drivers, sources of information and gastro-intestinal parasite control methods used by a group of New Zealand sheep farmers identified as low users of anthelmintic (AHC), and their perception of the efficacy and impacts of this approach. METHODS: A convenience sample of 13 sheep farmers farming with a policy of reduced AHC use (no pre-determined routine treatments of ewes >19 months old and/or lambs not routinely treated at pre-determined intervals from weaning through to late autumn) were identified. Semi-structured interviews were conducted regarding their farming philosophy, motivations for reducing AHC use, perceptions of the impacts of farming with reduced AHC use, and parasite control practices. Semi-quantitative data were analysed using descriptive statistics for demographic data and categorising participants' use of AHC and non-chemical control methods. Qualitative data regarding participants' motivations, approaches and rationale were analysed by systematic analysis of the transcripts and distillation of key concepts. RESULTS: Participants had been operating with reduced AHC use for 3 to ≥20 years. Key motivators for reducing AHC use were a diagnosis of anthelmintic resistance (AR) or concerns about AR developing. Parasite management information came from a wide range of sources. All respondents expressed overall positive views regarding the impacts of reduced AHC use but detailed information was not available.All identified that regular monitoring, based primarily on subjective animal and non-animal factors was important for their parasite control strategy. Most used faecal egg counts (FEC), often in an ad hoc manner. Five never treated adult ewes, two routinely treated ewes prior to lambing with short-acting AHC and the remainder occasionally treated a small number in low body condition. Four routinely treated some or all lambs at 28-30-day intervals from weaning to late autumn while the remainder based their treatment decisions for lambs on monitored information. All placed heavy emphasis on feeding sheep well, ensuring high post-grazing residuals, and cross-grazing. CONCLUSIONS: AR was a key motivator for participants to reduce AHC use, and a range of information sources and decision-making processes were used. Key parasite management practices were monitoring, primarily using subjective assessments, emphasis on feeding stock well and cross-grazing. CLINICAL RELEVANCE: The rising prevalence of AR will likely result in increasing the motivation for sheep farmers to reduce their AHC use. Veterinarians will play a key role in providing advice and assistance to facilitate changes in parasite management.Item Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics(MDPI (Basel, Switzerland), 2021-04-13) Ramadhani F; Pullanagari R; Kereszturi G; Procter J; Farooque AAMonitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to pro-duce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production.