The use of wind pumps for greenhouse microirrigation: a case study for tomato in Cuba.

Crop irrigation is a major consumer of energy. Only a few countries are self-sufficient in conventional non-renewable energy sources. Fortunately, there are renewable ones, such as wind, which has experienced recent developments in the area of power generation. Wind pumps can play a vital role in irrigation projects in remote farms. A methodology based on daily estimation balance between water needs and water availability was used to evaluate the feasibility of the most economic windmill irrigation system. For this purpose, several factors were included: three-hourly wind velocity (W3 h, m/s), flow supplied by the wind pump as a function of the elevation height (H, m) and daily greenhouse evapotranspiration as a function of crop planting date. Monthly volumes of water required for irrigation (Dr, m3/ha) and in the water tank (Vd, m3), as well as the monthly irrigable area (Ar, ha), were estimated by cumulative deficit water budgeting taking in account these factors. An example is given illustrating the use of this methodology on tomato crop (Lycopersicon esculentum Mill.) under greenhouse at Ciego de Ávila, Cuba. In this case two different W3 h series (average and low wind year), three different H values and five tomato crop planting dates were considered. The results show that the optimum period of wind-pump driven irrigation is with crop plating in November, recommending a 5 m3 volume tank for cultivated areas around 0.2 ha when using wind pumps operating at 15 m of height elevation.

Improving the low-wind performance of the AERMOD atmospheric dispersion model for predicting short-range impacts of livestock ammonia emissions.

Short-range impacts to sensitive ecosystems as a result of ammonia emitted by livestock farms are often assessed using atmospheric dispersion modelling systems such as AERMOD. These assessments evaluate mean annual atmospheric concentrations of ammonia and nitrogen deposition rates at the ecosystem location for comparison with ecosystem damage thresholds. However, predictions of mean annual atmospheric concentrations can be dominated by periods of stable night-time conditions, which can contribute significantly to mean concentrations. AERMOD has been demonstrated to overestimate concentrations in certain stable low-wind conditions and so the model could potentially overestimate the short-range impacts of livestock ammonia emissions. This paper tests several modifications to the parameterisation of AERMOD (v12345) that aim to improve model predictions in low-wind conditions. The modifications are first described and then are applied to three pig farm case studies in the USA, Denmark and Spain to assess whether the modifications improve long-term mean ammonia concentration predictions through improved model performance. For these three case studies, most of the modifications tested improved model performance as a result of reducing the long-term mean concentration predictions, with the largest effect for low- or ground-level sources (e.g. slurry lagoons or naturally ventilated housing).