Introducing Perennials into Grasslands in South West Australia Increases Gross Margins for Dual Purpose Merino Enterprises

Dual purpose Merino enterprises on the south coast of Western Australia (WA) typically utilise agricultural grasslands that comprise entirely of annual plant species. These grasslands face a range of challenges including a variable Mediterranean climate coupled with mostly infertile fragile sandy soils. As a consequence livestock producers have to manage potentially high supple-mentary feeding costs particularly in summer and autumn while running sufficient livestock to remain profitable. Sowing summer-active perennial species into these grasslands has been shown through short-term livestock trials to allow an increase in stocking rates and reduce the amount of supplement fed. The objective of this investigation was to use a validated GrassGro simulation to determine the highest gross margin (GM) system for a dual purpose Merino enterprise over a 41-year period in contrasting rainfall environments by varying a range of management factors. The hypothesis tested was that the addition of summer-active perennials would consistently raise GM in all rainfall environments simulated

Basal Cover of Perennial Native Grasses Increases Due to Seasonal Conditions

Australian native pastures in the high rainfall zone (\u3e 600 mm AAR) in northern Victoria and southern NSW are usually dominated by annual species, and occupy a considerable proportion of the landscape (Pearson et al., 1997; Hill et al., 1999). Productivity of native pastures can potentially be increased by using fertiliser (Lodge 1979; Garden and Bolger, 2001) but this nearly always comes at the expense of the native perennial grasses (Garden et al., 2000; Garden and Bolger, 2001). However, using a combination of fertiliser inputs and rotational grazing can provide increased productivity while maintaining the native perennial pasture base (Garden et al., 2003). Maintaining and improving the current native perennial pasture base in this hilly landscape is essential for maintaining ground cover and meeting natural resource management targets (Virgona et al., 2003). This experiment was conducted as part of the Ever Graze project (Avery et al., 2009), which had the aim of demonstrating that substantial increases in profitability can be achieved while improving environmental management by putting the Ever Graze Principle of ‘Right Plant, Right Place, Right Purpose, Right Management’ into action. The hypothesis for this research was that it is possible to maintain the persistence of native perennial grasses by appropriately combining fertiliser (superphosphate) application with appropriate grazing management

Development of a productivity asessment toll for native spotted gum forest on private land based on estimates of forest growth on Crown land.

Reliable estimates of forest productivity are essential for improved predictions of timber yields for the private native spotted gum resource in southern Qld and northern NSW. The aim of this research was to estimate the potential productivity of native spotted gum forests on private land by making use of available inventory data collated from Qld and northern NSW for spotted gum forest on Crown land (i.e. state forests). We measured a range of site-related factors to determine their relative importance in predicting productivity of spotted gum forest. While measures such as stand height and height-diameter relationships are known to be useful predictors of productivity, we aimed to determine productivity for a site where this information was not available. Through estimation of stand growth rates we developed a spotted gum productivity assessment tool (SPAT) for use by landholders and extension officers. We aimed to develop a tool to allow private landholders to see the benefits of maintaining their timber resource. This paper summarises the information used to develop the SPAT with a particular focus on forest growth relationships

Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p <0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p <0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management

Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm

Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties. The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available yield data. Maps of AWC with a resolution of 10 m were produced across a dryland grain farm in Australia. For certain years and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense dataset

Identification of climatological sub-regions within the Tully mill area

Identifying optimal nitrogen application rates that reduce nitrogen loss without adversely reducing yields would benefit growers and the environment. In order to identify optimal nitrogen application rates throughout the Tully mill area, it is important to identify sub-regions that share similar topographical, soil, farm management, productivity or climatological attributes. While current SIX EASY STEPS nitrogen guidelines enable a hierarchy of district, soil, block and crop nitrogen requirements for sugarcane, it would be beneficial for management zones to also take spatial climate variability information into account. Unfortunately, spatial climate variability within a region, is generally not considered when developing nitrogen management practices. The objective of this paper was to identify sub-regions within the Tully mill area based on climatological attributes as a first step towards better informing nitrogen management decisions. Rainfall, radiation and temperature data were obtained on a 0.05 by 0.05˚ grid (approximately 5 km by 5 km) for sugarcane-growing areas within the Tully Mill region. A K-means clustering algorithm was then used to cluster these grid cells into distinct sub-regions based on seasonal or annual climate data. Two distinct sub-regions were identified based on total annual rainfall and annual average daily radiation data. These sub-regions were identified as a northern and southern sub-region, divided roughly along the Tully River. The northern sub-region was characterised by lower radiation, lower temperatures and higher rainfall than the southern sub-region. Crop simulation models will now be able to use this knowledge to assess if nitrogen management plans should vary between the two sub-regions in Tully

FORECASTING MISSING DATA USING DIFFERENT METHODS FOR ROAD MAINTAINERS

Observations collected from meteorological stations that are available to road maintainers and used for experimental purposes in this paper. Unfortunately, these observations are insufficient to make good forecasting that is needed for road maintainers. Those meteorological stations are located next to the road surface in the territory of the Republic of Latvia. The road maintainers can make forecasting using this data what is needed for the winter months. It is up to the road maintainers in winter months to process decision-making on road surface smudging with anti-slip chemical materials. The missing data in each meteorological station exists from time to time. The paper represents the possibility of using several approaches to fill out these missing data. This process is needed to be more accurate in predicting specific parameters aggregated from meteorological stations. These approaches are compared between the three closest meteorological stations available in the Republic of Latvia. The relevant data are for the winter months of 2017-2018. To conclude which is more accurate with VAS "Latvijas valsts celi" data set

Geostatistical merging of weather radar data with a sparse rain gauge network in Queensland

Many parts of Australia, including much of Queensland and Northern Australia, tend to have sparse rain gauge coverage. To provide rainfall information across Australia, several gridded daily rainfall datasets such as those available through the Australian Water Availability Project and Scientific Information for Land Owners services have been developed. These daily grids are produced by interpolation of rain gauge data and therefore can provide unrealistic rainfall estimates in areas that have few rain gauges. To obtain rainfall data at a higher spatial resolution, weather radars and satellites can provide coverage over a large area although their measurements come with considerable uncertainty. Various approaches have been developed to adjust radar and satellite data and statistically merge them with rain gauge measurements in interpolation schemes, the goal being to retain the information on the spatial distribution of rainfall provided by remote sensing while also taking advantage of the greater accuracy of the rain gauges, but many of these techniques have been applied primarily on shorter time scales of an hour or less. This paper applies some existing methods for geostatistical merging of radar data with sparse rain gauge networks and evaluates the performance of the approaches using the Mt Stapylton radar in Brisbane and 15 surrounding rain gauges. Summer and winter data from 01/12/2013 to 28/02/2018 are considered. The radar data is corrected for mean field bias using quantile mapping and is used to develop the variogram models for use in Kriging. The performance of Kriging the gauge data using the radar variogram is compared with conditional merging and Kriging with radar values introduced as a drift variable. Leave-one-out cross-validation is used to evaluate the performance of the methods. We find some disagreement between all radar-based approaches and the validation gauge measurements with typical daily root-mean-square errors being between 10mm and 20mm for all approaches. Some outliers with substantially higher RMSE are noted for some days in the unadjusted radar data as well as in the corrected and interpolated data. For winter data the bias-correction and interpolation steps increased the agreement between the radar data and the validation gauges, but this improvement was not observed in the summer data. In addition, due to the low number of gauges the performance of the interpolation is extremely sensitive to the rain gauge values, with certain combinations of rain gauge values and choice of validation gauge leading to extremely large cross-validation errors. The results indicate that while incorporating the radar data makes it possible to perform Kriging with few gauges ona single day's data, this is not an ideal approach for quantitative precipitation estimation and further steps should be taken to improve the radar-gauge correlation

Processing Tomato Production in the Burdekin: Opportunities and Risks for Growers

The research undertaken here was in response to a decision by a major food producer in about 2009 to consider establishing processing tomato production in northern Australia. This was in response to a lack of water availability in the Goulburn Valley region following the extensive drought that continued until 2011. The high price of water and the uncertainty that went with it was important in making the decision to look at sites within Queensland. This presented an opportunity to develop a tomato production model for the varieties used in the processing industry and to use this as a case study along with rice and cotton production. Following some unsuccessful early trials and difficulties associated with the Global Financial Crisis, large scale studies by the food producer were abandoned. This report uses the data that was collected prior to this decision and contrasts the use of crop modelling with simpler climatic analyses that can be undertaken to investigate the impact of climate change on production systems. Crop modelling can make a significant contribution to our understanding of the impacts of climate variability and climate change because it harnesses the detailed understanding of physiology of the crop in a way that statistical or other analytical approaches cannot do. There is a high overhead, but given that trials are being conducted for a wide range of crops for a variety of purposes, breeding, fertiliser trials etc., it would appear to be profitable to link researchers with modelling expertise with those undertaking field trials. There are few more cost-effective approaches than modelling that can provide a pathway to understanding future climates and their impact on food production

A Validated Genome Wide Association Study to Breed Cattle Adapted to an Environment Altered by Climate Change

Continued production of food in areas predicted to be most affected by climate change, such as dairy farming regions of Australia, will be a major challenge in coming decades. Along with rising temperatures and water shortages, scarcity of inputs such as high energy feeds is predicted. With the motivation of selecting cattle adapted to these changing environments, we conducted a genome wide association study to detect DNA markers (single nucleotide polymorphisms) associated with the sensitivity of milk production to environmental conditions. To do this we combined historical milk production and weather records with dense marker genotypes on dairy sires with many daughters milking across a wide range of production environments in Australia. Markers associated with sensitivity of milk production to feeding level and sensitivity of milk production to temperature humidity index on chromosome nine and twenty nine respectively were validated in two independent populations, one a different breed of cattle. As the extent of linkage disequilibrium across cattle breeds is limited, the underlying causative mutations have been mapped to a small genomic interval containing two promising candidate genes. The validated marker panels we have reported here will aid selection for high milk production under anticipated climate change scenarios, for example selection of sires whose daughters will be most productive at low levels of feeding