Abstract: A sample of 93 liquid dairy manures from Lombardy in northern Italy was analysed to determine manure composition, and regression equations for the prediction of manure composition were developed. The manures had a mean (and standard deviation) dry matter content of 95 (32) g kg-1, with total Kjeldahl nitrogen, ammonium nitrogen, carbon, phosphorus and potassium concentrations of 3.75 (1.00), 1.48 (0.45), 37 (13), 0.65 (0.24) and 2.62 (0.77) g kg-1 [raw manure], respectively. It was concluded that the high variability in the composition of dairy manures is not compatible with the use of tabulated average values in nutrient management plans, and requires traditional or simplified analyses to reduce fertiliser input in the agro-ecosystems and to preserve crop yield. Linear regressions were used to estimate the organic matter and nutrient content of the manures from the dry matter content and the electrical conductivity. Because electrical conductivity was only measured on manures with dry matter less than 103 g kg-1, regressions based on electrical conductivity were developed on a reduced sub-set of 38 samples. Electrical conductivity is an acceptable predictor for ammonium nitrogen, with a coefficient of determination (R2) of 0.76. Carbon was well predicted based on dry matter with an R2 of 0.98, while phosphorus and total Kjeldahl nitrogen were estimated using both dry matter and electrical conductivity (R2=0.69 and 0.91, respectively). Unreliable predictions are obtained for potassium. It was concluded that the electrical conductivity and the dry matter content are the basic data required for low-cost estimates of manure nutrient concentrations and are useful to improve the effectiveness of nutrient management plans.
Abstract: Agro-ecological indicators are simple conceptual models to carry out agro-environmental assessments. Their use requires data that need to be obtained at low cost, frequently avoiding direct measurements. The quality of input data thus may limit the usefulness of the indicators. One source of uncertainty is the spatial interpolation of inputs, used to provide indicator values throughout an area. This paper explores the uncertainty of the inputs, and its effect on the output of one indicator, the phosphorus indicator (IP). The indicator evaluates the appropriateness of phosphorus (P) use by farmers, assigning bad scores to over- and under-fertilization. We evaluated its use for P management in the Sud Milano Agricultural Park (northern Italy). We used data contained in a large database of soil and farm properties as well as crop management information at the cadastral parcel level to calculate IP values. The uncertainty of a single input variable (extractable soil P) was tested to quantify the corresponding uncertainty of the indicator. The results show that the variability of IP is high and within 80% of the analyzed area excessive applications of P fertilizers are made, in particular in animal farms. In most cases, uncertainty is not relevant, as it is either very low, or (if high) it is related to extremely low indicator values: in these cases, the assessment of P management is unaffected by the uncertainty of the indicator. The results show that in this area P fertilizers should be applied at lower doses, or not applied at all. An extension service might help farmers with fertilizer management, reducing resource use, environmental pollution and costs. This study shows that uncertainty analysis is a crucial component of environmental assessments, and that the importance of uncertain input data needs to be evaluated on a case-by-case basis.
Abstract: Cropping systems in northern Italy are intensively managed, but an integrated environmental accounting of these systems has not been published yet. We conducted this study to evaluate cropping systems management in a study area in northern Italy using indicators. The study area is a regional agricultural Park, with cereal and livestock farms, cultivating mostly maize, rice, meadows, and winter cereals. To select the indicators, we identified for the study area the most relevant issues concerning the potential impact of agriculture on the environment: nutrient and pesticide management, use of fossil energy and soil management. Subsequently, we selected indicators from the literature, which could address these issues. We also added indicators describing the economic performance. The data were collected at the field level by periodic face-to-face interviews with seven farm managers over 2 years. Indicators were calculated for all crops cultivated in each field (n = 266). According to the methodology proposed, the best economic performance (gross margin) was obtained by rice, followed by maize, winter cereals, and forage crops. Nitrogen and phosphorus surpluses were high for maize (due to a large use of animal manures), and moderate for rice and permanent meadows (where mineral fertilisers are not usually applied). Maize used high fossil energy inputs; however, the output/input ratio (an indicator of the dependence of food and feed production on non-renewable energy) was elevated, due to high aboveground biomass production. The potential impact due to pesticide use (evaluated with indicators that consider the toxicity and the exposure to active ingredients) was relevant only for rice, moderate for maize and other cereals, and null for forages. Finally, soil management was evaluated for the 2-year crop succession on each field (n = 131): permanent meadows are excellent (due to continuous soil cover and large returns of organic carbon to soil), rice-based successions are unsatisfactory (due to low residues and manure application and continuous cropping), and maize successions are intermediate. This work shows that good quality data can be collected on-farm for economic and environmental accounting at field level. The indicators chosen for the analysis describe a range of issues in the study area, and make it possible to clearly separate and characterise different cropping systems. The procedure for their calculation is transparent and sound, and can be applied for ex-ante, ex-post, and monitoring procedures.
