Pig effluent-P application can increase risk of P transport: two case studies

Land application of piggery effluent (containing urine, faeces, water, and wasted feed) is under close scrutiny as a potential source of water resource contamination with phosphorus (P). This paper investigates two case studies of the impact of long-term piggery effluent-P application to soil. A Natrustalf (Sodosol) at P1 has received a net load of 3700 kg effluent P/ha over 19 years. The Haplustalf (Dermosol) selected (P2) has received a net load of 310 000 kg P/ha over 30 years. Total, bicarbonate extractable, and soluble P forms were determined throughout the soil profiles for paired (irrigated and unirrigated) sites at P1 and P2, as well as P sorption and desorption characteristics. Surface bicarbonate (PB, 0 - 0.05 m depth) and dilute CaCl2 extractable molybdate-reactive P (PC) have been significantly elevated by effluent irrigation (P1: PB unirrigated 23±1, irrigated 290±6; PC unirrigated 0.03±0.00, irrigated 23.9±0.2. P2: PB unirrigated 72±48, irrigated 3950±1960; PC unirrigated 0.7±0.0, irrigated 443±287 mg P/kg; mean±s.d.). Phosphorus enrichment to 1.5 m, detected as PB, was observed at P2. Elevated concentrations of CaCl2 extractable organic P forms (POC; estimated by non-molybdate reactive P in centrifuged supernatants) were observed from the soil surface of P1 to a depth of 0.4 m. Despite the extent of effluent application at both of these sites, only P1 displayed evidence of significant accumulation of POC. The increase in surface soil total P (0 - 0.05 m depth) due to effluent irrigation was much greater than laboratory P sorption (>25 times for P1; >57 times for P2) for a comparable range of final solution concentrations (desorption extracts ranged from 1-5 mg P/L for P1 and 50-80 mg P/L for P2). Precipitation of sparingly soluble P phases was evidenced in the soils of the P2 effluent application area.

Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.