1,721,110 research outputs found
Methodological Aspects of On-Farm Monitoring of Cropping Systems Management for Sustainability Assessments
No full textTo conduct agro-environmental assessments at field and farm scale, detailed management data of crop and animal production systems are needed. However, this type of data is only rarely collected by public administrations. In the period 2005-2006, we made an experience of on-farm monitoring of cropping systems management, within a larger project aimed at assessing sustainability of agricultural systems in Italian Parks. In this paper, we describe and discuss the steps taken to carry out periodic face-to-face interviews in farms in the Sud Milano Agricultural Park (northern Italy). The first step was the selection of seven farms, which we identified by applying cluster analysis at a large database describing 733 farms of the Park. After having identified the most relevant agro-environmental issues in the studied area, we established a list of simple but sound indicators to evaluate the effects of agricultural management on the environment. The criteria used to select the indicators were that they should: be calculated on easily available data, not be based on direct measurements, make a synthesis of different aspects of reality, and be easily calculated and understood. The indicators selected evaluate nutrient management, fossil energy use, pesticide toxicity, soil management, and economic performance. Subsequently, we designed a data model to store input data used to calculate the indicators (farm configuration, flows of materials and money through the farm gate, animals and their rations, history of crop cultivation, crop management). The data model that we obtained is relatively complex, but adequate to store and analyse the large amount of data acquired during the two-year project. A questionnaire was developed to fully comply with the indicators selected and the data model. The questionnaire was used to carry out approximately six interviews per farm each year, with an investment of time of 1-2 hours per interview. Appropriate double checks of data collected in the interviews were put in place to ensure a good data quality. The data collected were used for the calculation of several agro-ecological indicators. The results show that nutrient management in maize is not satisfactory due to high surpluses, while meadows have the lowest surplus. The fertilisers and diesel consumption are the most important energy inputs to maize, while their importance is lower for the other crops. Seeds and fertilisers are the main costs for maize and winter cereals, while diesel consumption represents a large part of the economic costs for meadows; pesticides are the principal costs in rice. We concluded by identifying steps for further research
Integrated sustainability assessment of cropping systems with agro-ecological and economic indicators in northern Italy
No full textThe sustainability of agricultural systems is frequently evaluated with indicators, which are synthetic variables describing complex systems. Each indicator deals with one aspect of sustainability (e.g. nutrients, pesticides, energy), and therefore the result of a complete assessment usually includes several indicator values. These values are frequently presented separately, while an integrated evaluation could benefit from the calculation of a single sustainability index. The aim of this work was to integrate 15 economic and environmental indicator values into a global sustainability index (Sg) ranging from 0 to 1.
To calculate the indicators, we used a large data set of cropping systems management for 131 fields cultivated with arable crops in northern Italy, obtained through periodic interviews with farmers over a 2-year period. The fields were chosen to represent the main cropping systems in the area (cereals and forages, on animal and cereal farms). The 15 indicators describe a large variety of sustainability aspects, i.e. the economic performance and the management of energy, nutrients, soil, and pesticides.
The indicator values were first converted into a sustainability score (Si; 0-1) applying continuous non-linear sustainability functions that use thresholds defining what is sustainable, unsustainable, or intermediate. We obtained 15 values of Si per each field, which we aggregated into Sg using indicator-specific weights provided by different stakeholders. This procedure permits not only the single indicators evaluation, but also to combine indicators for an assessment of cropping systems at field level.
Permanent meadows, due to good management of soil, pesticides and nutrients, obtained the highest Sg, even when different weights were used. Continuous rice obtained the lowest Sg (due to unsatisfactory soil management, low energy production, and high pest and weed pressure, which involved a large use of pesticides), while maize was intermediate, with good economic and energetic performance.
The methodology allows a transparent, repeatable, sound, and quantitative evaluation of sustainability of agricultural systems. It can be easily expanded by adding other indicators, and can be tailored by changing the thresholds used to calculate Si and the weights assigned by stakeholder groups
A preliminary evaluation of the simulation model CropSyst for alfalfa
No full textThis work stems from the need to set-up appropriate simulation models for scenario analysis of intensive forage cropping systems in northern Italy, where alfalfa plays a major role. CropSyst is a deterministic, process-based, with daily time-step cropping systems simulation model. It can simulate crop growth and development, water and nitrogen balance for herbaceous annual and perennial crops. In this work, it was used to simulate aboveground biomass (AGB) accumulation and soil water content (SWC) for two alfalfa meadows seeded in 1996 and 1997 in Lodi, northern Italy (45°N latitude). The crop was parameterised with data from the literature, local experience and calibration with measured data from the first 2 years. Data from the third year were used for validation.
The cumulative yields of the 3-year periods were 38.2 and 36.9 t AGB ha−1, obtained with a total of 14 cuts. The set of crop parameters is consistent with values reported in the literature. For most of the cuts, the model simulates appropriately the growth of the crop: the relative root mean squared error (RRMSE) between observed and measured AGB ranged between 3 and 6% after calibration and between 3 and 5% after validation. RRMSE for SWC ranged between 13 and 21% after calibration and between 10 and 20% after validation. Even if some limitations are explicitly addressed, this crop parameter set can be already used for explorative scenario simulations in the study area. This work has demonstrated the robustness of the model for perennial forage crops simulations and has suggested some improvements of the model (automatic scheduling of cuts, role of crown reserves)
La modellistica dei sistemi colturali utilizzata per interpretare il funzionamento del sistema suolo-coltura e proporre interventi di mitigazione dell'inquinamento da nitrati
Full text linkOn-farm monitoring of economic and environmental performances of cropping systems: Results of a 2-year study at the field scale in northern Italy
No full textCropping 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
Multi-objective optimisation of a model of the decomposition of animal slurry in soil : Tradeoffs between simulated C and N dynamics
No full textTo formulate best management practices for animal slurry, it is important to understand and predict its decomposition in the soil. Slurry decomposition dynamics can be studied by measuring CO2 fluxes and soil mineral nitrogen concentration during laboratory incubations and subsequently calibrating a simulation model. Carbon and nitrogen dynamics are linked and both should be properly simulated. In this work we wanted to identify the tradeoffs between errors in the simulation of C respiration and of soil inorganic N concentration.
We optimised six parameters of CN-SIM (a mechanistic dynamic simulation model), using data of respired C and soil inorganic N measured during a 180-day laboratory incubation of five dairy slurries on three soils. Optimisation was carried out with a multi-objective genetic algorithm (NSGA-II), by minimising the Relative Root Mean Squared Error (RRMSE) between observations and simulations.
The simulation of C respiration was frequently conflicting with the simulation of inorganic N, i.e. low RRMSE–CO2 corresponded with high RRMSE–N and vice versa. When minimising RRMSE–CO2 a set of parameters was obtained that enhanced microbial N immobilisation and reduced the turnover of the organic pools, to match the observed decrease of inorganic N in the 28 days after slurry addition to soil. Remineralisation occurring in the following 150 days caused a marked overestimation of inorganic N. When minimising RRMSE–N, the optimisation provided parameters that strongly reduced remineralisation of immobilised N by markedly diminishing C respiration, with a consequent underestimation of CO2 emission. A modified version of the model, containing a simple implementation of denitrification and of clay fixation/release of ammonium, performed better than the original model for most treatments.
We conclude that the mineralisation/immobilisation turnover in the model is not fully adequate to represent C and N dynamics. We also discuss the implementation of changes (time-varying microbial efficiency and C to N ratio; simulation of ammonium clay fixation and emissions of N2/N2O) to improve model performance
Valutazioni territoriali dei PUA e dei PUAS in Regione Lombardia
Full text linkIl software VA.TE. consente di simulare le dinamiche dell'acqua e dell'azoto nel sistema suolo-coltura e di identificare soluzioni a basso impatto ambientale per la gestione agronomica dei reflui zootecnici in aziende con allevamenti. Il software integra un modello dinamico di simulazione dei sistemi colturali (CropSyst) con alcuni database agro-ambientali lombardi (meteorologici, pedologici, allevamenti). Il software consente la preparazione automatica delle simulazioni, la loro esecuzione e la visualizzazione tabellare e grafica dei risultati. E' rivolto ai tecnici che si occupano della redazione o della valutazione dei piani di utilizzazione agronomica dei reflui zootecnici (PUA e PUAS) in Lombardia
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