Evaluating the Benefits of Restricted Grazing to Protect Wet Pasture Soils in Two Dairy Regions of New Zealand

Many dairy farms in the Manawatu and Southland regions of New Zealand have poorly drained soils that are prone to treading damage, an undesirable outcome on grazed pastures during the wetter months of the year. Removing cows to a stand-off pad during wet conditions can reduce damage, but incurs costs. The objective of this study was to evaluate the impact of different levels of restricted grazing (from 0 to 10 hours grazing time/day for lactating cows) on pasture yield, damage and wastage, feed and stand-off expenses, and farm operating profit. A simulated farm from each region was used in a farm systems model. This model simulated pasture-cow-management interactions, using site-specific climate data as inputs for the soil-pasture sub-models. Days to recover previous yield potential for damaged paddocks can vary widely. A sensitivity analysis (40 to 200 days to recover) was conducted to evaluate the effect of this parameter on results. Full protection when there is risk of damage (0 grazing hours/day) appeared to be less profitable compared with some level of grazing, because the advantages of reduced damage were outweighed by the disadvantages of managing infrequently grazed pastures. The differences in operating profit between full protection and some level of grazing became less as the recovery time increased, but for both regions grazing durations of 6-8 hours/day when a risk of damage is present appeared to be a sensible strategy irrespective of recovery time

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Designing profitable and climate-smart farms using virtual reality.

Many pastoral farmers are searching for ways to lower the carbon emission footprint that is generated by livestock. Planting trees on the farm is currently a popular option for farmers to offset their emissions yet requires knowledge of suitable tree species and locations to plant them. This paper describes a decision-support tool aimed at helping farmers to create and visualise different planting designs while balancing the objectives of sequestering carbon and maintaining farm profitability. We take an innovative approach by combining virtual reality technology with biophysical models to create an environment where the user can actively create virtual future farm scenarios. Through the creation process, the user can simultaneously balance multiple objectives including farm aesthetics, economic returns, business and environmental ambitions, and carbon emissions (net) balance. For this proof-of-concept study, we incorporate virtual reality technology in Unreal Engine, environmental and financial data, and high-resolution spatial layers from an operational 400-hectare livestock farm in New Zealand

Irrigation Control through Acoustic Proximal Sensing of the Onset of Surface Water

Irrigation is a useful crop enhancement procedure up to the point where free surface water appears. However, over-irrigation can lead to an accumulation of free water on the soil surface, which in turn results in overland flow and a high risk of contaminant loss. The current work addresses the problem of measuring free water on the surface of agricultural soils by a real-time acoustic remote sensing method. Directional acoustic transmitter and receiver arrays are used to define a &ldquo;footprint&rdquo; on the ground from which changes in reflectance are sensed. These arrays are mounted on a moving irrigator. Chirp signals are used to provide along-path resolution and to ensure robustness against unwanted acoustic background noise from farm machinery and the irrigator. Field measurements have been conducted above a well-defined &ldquo;quadrat&rdquo; with controlled and measured water content and also with the instrument mounted on an operational irrigator. A structured light camera mounted above the footprint is used to validate surface water fraction. It is found that the areal fraction of free water on the soil surface can be reliably estimated from changes in the amplitude of the reflected sound waves. The mechanism giving rise to the observed acoustic reflectivity changes is discussed and a model is developed which agrees with normalized intensity observations with a coefficient of determination R2 between 0.65 and 0.83. The rms error between model predictions and observations is comparable to the rms variation of the measurements, indicating that there is insignificant error due to the choice of model

Management of recycled water for sustainable production and environmental protection : a case study with Northern Adelaide Plains recycling scheme

In South Australia, 95,000 megalitres (ML) of municipal wastewater is collected and treated in metropolitan Adelaide. Approximately 50 % of this volume is treated at the Bolivar wastewater treatment plant (WWTP) to produce a high quality wastewater suitable for irrigation without health related restriction to vegetable and salad crops. Following treatment wastewater is piped to horticultural growers on the Northern Adelaide Plains through Virginia Pipeline Scheme (VPS). The establishment of the VPS is not only effective in reducing the amount of wastewater entering the Gulf St Vincent but also facilitates the recycling of otherwise waste water for irrigation purposes. The VPS is the largest recycled water scheme in Australia serving around 250 horticultural growers. This paper provides an overview of the scheme focusing on the level of wastewater treatment at Bolivar WWTP, the value of the treated water as a source of irrigation water, carbon and nutrients for crop growth, and the socio-economic and environmental implications of its use for irrigation.

Designing profitable and climate-smart farms using virtual reality

Many pastoral farmers are searching for ways to lower the carbon emission footprint that is generated by livestock. Planting trees on the farm is currently a popular option for farmers to offset their emissions yet requires knowledge of suitable tree species and locations to plant them. This paper describes a decision-support tool aimed at helping farmers to create and visualise different planting designs while balancing the objectives of sequestering carbon and maintaining farm profitability. We take an innovative approach by combining virtual reality technology with biophysical models to create an environment where the user can actively create virtual future farm scenarios. Through the creation process, the user can simultaneously balance multiple objectives including farm aesthetics, economic returns, business and environmental ambitions, and carbon emissions (net) balance. For this proof-of-concept study, we incorporate virtual reality technology in Unreal Engine, environmental and financial data, and high-resolution spatial layers from an operational 400-hectare livestock farm in New Zealand.  NZBIDA</p