Indrakumar Vetharaniam

Varroa destructor (Anderson & Trueman, 2000) is a significant parasite of Apis mellifera (honey bee; Linnaeus, 1758) worldwide (Le Conte et al., 2010; Rosenkranz et al., 2010). Varroa are ectoparas...

European canker (EC) caused by the fungal pathogen Neonectria ditissima, causes serious tree health and production problems in many apple-producing regions, particularly in high-rainfall climates. Prediction models based on biological and epidemiological understanding of disease processes can improve efficiency of disease management practices. We developed a mathematical model for the EC disease cycle that targets critical mechanisms in the epidemiology of the disease, especially the pathogen response to meteorological parameters. The model enables infection risk comparisons between regions and seasons and facilitates decision support for disease management. The responses of disease cycle processes to environmental factors were determined from experimental and published data, including infection response to temperature, rainfall and tree disease incidence; and spore production potential in relation to lesion drying. Sensitivity analyses showed that predicted risk was most sensitive to changes in the optimal temperature parameter, while changing rainfall and drying thresholds had relatively little effect. Indices for daily and annual accumulated infection risk revealed variation between regions and years, helping to illustrate the changing pressures to which management actions need to adapt. Field validation was not part of this project, and we recommend international comparisons for better calibration and validation to support application opportunities for EC infection risk prediction.

Regional suitability for growing avocados and blueberries may alter with climate change. Modelling can provide insights into potential climate change impacts, thereby informing industry and government policy decisions to ameliorate future risks and capitalise on future opportunities. We developed continuous/sliding-scale models that used soil, terrain and weather data to assess location suitability for cultivating avocado and blueberry, based on physiological and phenological considerations specific to each crop. Using geographical information system (GIS) data on soil, slope and weather, we mapped cultivation suitability for avocado and blueberry across New Zealand, and, for accuracy, “ground-truthed” these maps in an iterative process of expert validation and model recalibration. We modelled the incremental changes in location suitability that could occur through climate change using “future” GIS-based weather data from climate model simulations for different greenhouse gas (GHG) pathways that ranged from stringent GHG mitigation to unabated GHG emissions. Changes in maps over time showed where suitability would increase or decrease and to what extent. These results indicate where avocado and blueberry might replace other crops that become less suitable over time, and where avocado might displace blueberry. The approach and models can be applied to other countries or extended to other crops with similar growing requirements.

This study explores the potential benefits of transitioning from livestock farming to high-value alternative crops as a strategy for mitigating agricultural greenhouse gas (GHG) emissions in New Zealand. The government has set ambitious targets for reducing methane (CH₄) emissions. However, since animal product exports play a crucial role in New Zealand’s economy, any shift in land use will have significant impacts on both the regional and national economy. We developed a GIS framework that integrated (i) growing requirements, (ii) GHG emissions and (iii) profitability for crops. Analysis of export market opportunities identified twelve high-value “alternative crops”. Availability of suitable land for crop expansion was not a limiting factor. Working with the Ministry for Primary Industries, we explored how land use change scenarios contributed to Government 2050 biogenic CH₄ emission reduction targets. Doubling the area of alternative crops (a 195,000-ha increase) by reallocating land from livestock farming resulted in reducing biogenic CH₄ emissions by 1.2 to 5.4% (0.35 to 1.57 Mt CO₂-e) compared to 2017 baseline values, contributing to between 2.6 and 22.5% of the 2050 CH₄ reduction targets, simultaneously increasing profitability by $NZ1.25 to 1.32 billion annually. While this approach demonstrates potential benefits of land use change, a deeper understanding of the complexity of land use decision-making is required to enable successful transitions. Addressing barriers to change requires collaborative efforts from land users, researchers and policy makers.

The threats and opportunities faced by primary production industries from future climate changes can be adequately prepared for only with the guidance of model projections that can assist the development of robust policy and climate adaptation plans by governments and industries. We used continuous suitability models capable of reflecting incremental changes to project the suitability of locations across New Zealand for cultivating apple and kiwifruit in the mid- and late-century. These projections used future weather data from climate model simulations for two contrasting greenhouse gas (GHG) pathways: stringent GHG mitigation and unabated GHG emissions. To improve the suitability of the modelled temperature data, specifically for use with biologically driven, crop suitability models, we developed new bias-variance adjustments that preserved climate change signals within the data. Preliminary projections of land use across a range of alternative primary industries were obtained from a multinomial logit model incorporating continuous suitability scores as predictors. We refined the preliminary land-use projections by providing them as inputs into a simulation model of land use incorporating other drivers and constraints. This methodology provides a means for projecting future land use and the spatial footprints of primary industries, based on biological and econometric considerations, under different modelled climate change scenarios.

Under climate change, land use suitability for horticultural production will change; this has prospects of both adverse socio-economic impacts for the industry in some regions, and beneficial impacts in others. Policy development and industry guidance are needed to develop adaptations to mitigate climate change risks and exploit new opportunities. For climate-change issues, models provide a powerful means for assessing future suitability at a patch, region or national scale in order to guide policy decisions. Here, we describe the development of a new continuous (sliding-scale) suitability modelling approach to assess the suitability of different locations for growing apple and kiwifruit in New Zealand, based on phenological and physiological considerations; these models used geographical information system (GIS) soil, land and weather data to develop maps showing the suitability of locations across New Zealand for cultivating apple and kiwifruit. The models were “ground-truthed” in an iterative process of expert parameterisation and recalibration to ensure maps aligned with current growing locations for the two crops. We estimated an econometric logit model that incorporated the continuous suitability scores as predictors of land use for apple and kiwifruit. Comparison of modelled suitability scores with industry-supplied maps of apple and kiwifruit orchards showed good consistency between predicted suitability and current land use. Compared with a range of alternative land uses, suitability for apple was highest for locations currently used to grow apple and suitability for kiwifruit was highest for locations currently used to grow kiwifruit. Our framework provides the capability to project incremental changes in the suitability of locations for apple and kiwifruit under different climate change pathways and to project consequential changes in their spatial footprints; this framework can be extended to other crops.

The sheep rumen submodel MollyRum14 was evaluated on its methane and VFA predictions against data from respiration-chamber trials conducted with sheep fed perennial ryegrass, white clover, chicory, forage rape, turnip (leafy and bulb varieties), swedes, kale, or forage radish. We assessed the model’s response to substrate degradation rate (settings that affect the rate of cellulose and hemicellulose digestion) and to fermentation stoichiometry (settings that alter nonglucogenic to glucogenic short-chain fatty acid ratios). Model predictions were evaluated against data for methane production (pCH₄: g/d), methane yield (yCH₄: g/kg DMI), and acetate to propionate ratio (A:P). The predictive ability of the model for both pCH₄ and yCH₄ was superior for perennial ryegrass than for other forages. Except for swedes and chicory, predictions for yCH₄ were correctly ranked across the forages evaluated. Except for forage rape, robust predictions were obtained for all forages using fast degradation kinetics and a predominantly acetogenic stoichiometry. Model predictions for forage rape were enhanced using slow degradation kinetics and a predominantly propionic stoichiometry. These results indicate that MollyRum14 is suitable to predict methane emissions from sheep fed a variety of fresh forages including annual fodder crops. However, a clear understanding of degradation rates and stoichiometries is needed to enhance the utility of the model as a predictive tool. This would allow continuous adjustment of digestion rates and stoichiometries to be potentially tailored to individual forage species.

The sensation of prickle from textile garments is directly related to the force that a fiber protruding from the fabric surface can exert on the skin without buckling – its critical buckling load (CBL). Finite element modeling (FEM) has previously been used in the literature to predict CBLs for a set of 25 fibers with different along-fiber morphology. With a view to high-throughput analysis of fibers, we investigated two analytical methods that were potentially faster and less computationally intensive than FEM and applied them to calculate CBLs for the same set of fibers. In addition, we tested a numerical integration and gradient search (NIGS) method that we developed by adapting a previously published, non-FEM, numerical approach. The analytical methods that we tested were either inadequately formulated or prone to instability. Our NIGS method was more reliable that the analytical methods (but slower to compute), and its results appeared more accurate than the published FEM results, based on an inconsistency metric that we developed. The published FEM results and the NIGS predictions agreed within 5% for 60% of the fibers, and within 10% for 72% of the fibers (with differences ranging from 0.4% to 19.1%) and generally showed qualitative agreement on the response of CBL to fiber shape, with some notable exceptions. The response of CBL to dimensional variation was complex. This, and the inconsistency between methods, highlights the need for caution when analyzing complicated biological structures, such as wool, and the value of verifying the reliability of any predictions from any approach.

In dairy cows, mammary gland involution, and thus a decline in milk production, occurs following peak lactation. To examine the cell signaling pathways regulating involution of the mammary gland, signal transducer and activator of transcription factors (STAT5 and 3), suppressors of cytokine signaling (SOCS1-3 and CIS), insulin-like growth factors (IGF1 and 2), and protein kinase B (Akt) were examined. Mammary involution was induced by termination of milking, and alveolar tissue was collected from 52 nonpregnant, primiparous, mid-lactation Holstein-Friesian cows killed at 0, 6, 12, 18, 24, 36, 72, and 192h postmilking. Qualitative immunohistochemistry showed that activated (phosphorylated) STAT5-P was localized in nuclei of mammary epithelial cells at the early time points, with detection levels decreasing by 24h postmilking. In contrast, STAT3-P was barely detectable at the early time points, with detection levels increasing following longer postmilking periods. This was supported by Western analysis, which showed a decline in STAT5 and STAT5-P protein levels by 24h postmilking, no change in STAT3 levels, and an increase in STAT3-P protein (barely detectable at the early time points) by 72h postmilking. Quantitative real-time reverse transcription PCR analysis showed SOCS1 and SOCS3 mRNA increased by 72h postmilking compared with 6h postmilking. The SOCS2 mRNA remained unchanged across the time series, whereas CIS decreased by 18h postmilking and remained lower compared with that at 6h postmilking until 72h postmilking. The IGF1 mRNA increased by 192h postmilking, whereas IGF2 mRNA decreased by 18h postmilking compared with 6h postmilking. The IGFBP5 mRNA and protein levels of Akt and Akt-P remained unchanged over the time series. These results show that reciprocal activation of STAT5 and STAT3 occurs at the onset of mammary gland involution in the bovine, albeit at a slower rate than in rodents. Mathematical modeling of the pathways indicated that activated STAT3 could block the STAT5 pathway by upregulating SOCS3. The regulation of IGF1-Akt signaling suggests that by 192h postmilking in dairy cows, the involution process is still in the reversible phase, with quiescent mammary epithelial cells not yet in the senescent phase.