Marco Acutis

SOILPAR 2.00: software to estimate soil hydrological parameters and functions

SOILPAR 2 is a program for estimating soil parameters. It allows: (1) storing soil data in a georeferenced database, (2) computing estimates of soil hydrological parameters using 15 procedures, (3) comparing the estimates against measured data using both statistical indices and graphics, and (4) creating maps using the ESRI format. An interface to/from Excel and CropSyst is provided. Eleven methods estimate one or more of the following characteristics: soil water content at predefined soil matrix tension, saturated hydraulic conductivity, and bulk density. Three methods estimate the parameters of well-known soil water retention functions (Brooks-Corey, Hutson-Cass, van Genuchten), and one estimates both saturated soil hydraulic conductivity and the soil water retention curve parameters (Campbell). The software runs under Windows 98/NT/2000/XP and is freely downloadable via internet.

irene: a software to evaluate model performance

Abstract The software irene (Integrated Resources for Evaluating Numerical Estimates) is a data analysis tool designed to provide easy access to statistical techniques for use in model evaluation. Mostly, non-replicated model estimates ( E i ) are compared against non-replicated measurements ( M i ). The software also allows comparing individual estimates against replicated measurements (or vice versa) and replicated estimates against replicated measurements. The evaluation of model performance is essentially based on the difference E i − M i , or on the correlation–regression of E i vs. M i (or vice versa). In addition, model evaluation by probability distributions, pattern analysis, or fuzzy-based aggregation statistics is allowed. Graphics are included in most analytical tasks. The results are displayed in separate spreadsheets and can be exported into MS Excel workbooks.

A component-Based Framework for Simulating Agricultural Production and Externalities

Although existing simulation tools can be used to study the impact of agricultural management on production activities in specific environments, they suffer from several limitations. They are largely specialized for specific production activities: arable crops/cropping systems, grassland, orchards, agro-forestry, livestock etc. Also, they often have a restricted ability to simulate system externalities which may have a negative environmental impact. Furthermore, the structure of such systems neither allows an easy plug-in of modules for other agricultural production activities, nor the use of alternative components for simulating processes. Finally, such systems are proprietary systems of either research groups or projects which inhibits further development by third parties.

Stochastic use of the LEACHN model to forecast nitrate leaching in different maize cropping systems.

This paper proposes a stochastic application of a deterministic model (LEACHN) with the aim of forecasting the probability of exceeding given nitrate leaching levels for different cropping systems and soil hydrological characteristics. The understanding of the level of probability associated to the prediction of leaching is an important criteria for the judgement of cropping systems. After calibration of organic matter mineralization and nitrification rates in both a sandy-loam and a loamy soil of the Western Po river valley (Northern Italy), LEACHN was used as a stochastic tool to evaluate the meteorological variability and the spatial variability of hydrological parameters of a soil. Meteorological variability was generated using series of measured air temperature, rainfall and global radiation for a period of at least 15 years, and these were then expanded to 100 years using the climate simulator CLIMGEN. Soil variability was simulated using the scale factor approach. The scale factor mean and standard deviation were obtained in eight locations within a 1000-m 2 area. The stochastic scale factors were applied to parameters a and b in Campbell’s water retention function and to hydraulic conductivity. The following combinations of crops were simulated in the two soils: (a) continuous maize for silage (MM); (b) continuous maize for grain (MG); (c) a combination of late harvested Italian ryegrass and short cycle silage maize (LRM); and (d) early harvested Italian ryegrass and late maturing silage maize (ERM). The crops were fertilized with 200 or 300 kg N ha 1 year 1 (or 450 kg for MM) and submitted to three water regimes: no irrigation, irrigation on the basis of water balance and conventional irrigation, which resulted in the highest volume. The simulated leaching was higher when fertilization and irrigation inputs were higher. It was further reduced by the introduction of cover-crop and was higher in the sandy soil. All these factors interacted, creating different levels of nitrate loss risk, that ranged from a minimum leaching of 4 kg N ha 1 year 1 , with a 10% breakthrough probability of 16 kg N ha 1 year 1 (low fertilized and irrigated ERM in the sandy-loam soil) to a maximum average leaching of 146 kg N ha 1 year 1 with a 10% breakthrough probability of 235 kg N ha 1 year 1 (high fertilized, conventionally irrigated MM in the sandy soil). The breakthrough probability curves associated to nitrate leaching are skewed, showing that lower than average values are more frequent than higher ones. The standard deviations of yearly leaching were closely correlated to the

Evaluation of mitigation strategies to reduce ammonia losses from slurry fertilisation on arable lands.

To evaluate the best practices in reducing ammonia (NH3) losses from fertilised arable lands, six field trials were carried out in three different locations in northern Italy. NH3 emissions from cattle slurry were estimated considering the spreading techniques and the field incorporation procedures. The measurements were performed using long term exposure samplers associated to the determination of the atmospheric turbulence and the use of the backward Lagrangian stochastic (bLS) model WindTrax. The results obtained indicate that the NH3 emission process was exhausted in the first 24-48 h after slurry spreading. The slurry incorporation technique was able to reduce the NH3 losses with respect to the surface spreading, where a contextual incorporation led to reductions up to 87%. However, the best abatement strategy for NH3 losses from slurry applications has proved to be the direct injection into the soil, with a reduction of about 95% with respect to the surface spreading. The results obtained highlight the strong dependence of the volatilisation phenomenon by soil and weather conditions.

Dual-porosity and kinematic wave approaches to assess the degree of preferential flow in an unsaturated soil

Abstract The purpose of this study was to assess the degree of preferential flow in an unsaturated soil column using two different models: the dual-porosity model, MACRO, and the kinematic wave approach (KWA) based on boundary-layer flow theory. The soil column experiments consisted of six infiltrations with intensities varying from 15 to 101 mm h−1. Bromide solution was also infiltrated at an intensity of 79 mm h−1 and a concentration of 80 mg l−1. Both MACRO and the KWA indicated the absence of pure preferential flow. The KWA indicated intermediate flow with dispersion of the wetting front with depth, whereas MACRO indicated flow dominated by the diffusion of capillary potential. These results shed light on the transition between flows dominated by momentum dissipation and by diffusion of capillary potential. The absence of pure macropore flow in the structured sandy soil is mainly due to efficient lateral mass exchange in this material.

Precision determination for the dynamic respirometric index (DRI) method used for biological stability evaluation on municipal solid waste and derived products

Dynamic respiration index (DRI) is an effective respirometric method to measure the biological stability of municipal solid waste (MSW). It allows testing MSW biological stability under standardized conditions and is now used as a routine analytical method. However, the method needs to be studied for precision parameters to ensure the quality of results generated. This work reports on a DRI validation study, detecting repeatability (r) and reproducibility limits (R). To perform the study, 4-6 Italian laboratories took part in an interlaboratory test for the validation of the DRI method on four different municipal solid wastes from different mechanical-biological treatment full-scale plants. Precision values (r and R) of DRI, expressed as relative standard deviation, were in the range of 3.6% and 15.5%, and were acceptable when compared with previous data obtained in another respirometric test. On the other hand, no regressions were found between r and R, and DRI, and as a consequence prediction of precision values was not possible a priori for different DRI levels, unless the same typology of waste was considered.

Short communication: Resampling-based software for estimating optimal sample size

The SISSI program implements a novel approach for the estimation of the optimal sample size in experimental data collection. It provides a visual evaluation system of sample size determination, derived from a resampling-based procedure (namely, jackknife). The approach is based on intensive use of the sample data by systematically taking sub-samples of the original data set, and calculating mean and standard deviation for each of sub-samples. This approach overcomes the typical limitations of conventional methods, requiring data-matching statistical assumptions. Visual, easy-to-interpret provisions are supplied to display the variation of means and standard deviations as size of generated samples increases. An automatic option for identification of optimal sample size is given, targeted at the size for which the rate of change of means becomes negligible. Alternatively, a manual option can be applied. An ideal application of SISSI is in supporting the collection of plant and soil samples from field-grown crops, but it also holds potential for more general application. SISSI is developed in Visual Basic and runs under the Windows operating systems. The installation software package includes the executable files and a hypertext help file. SISSI is freely available for non-profit applications.

Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region : the role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling

SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0-0.3m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (RS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-R2 (0.63-0.69) and low uncertainty (s.d.<0.76gCkg-1 with RS, and <1.25gCkg-1 without RS). These outputs allowed depicting a time variation of SOC at 1arcsec. SOC estimation strongly depended on the soil texture, land use, rainfall and topographic indices related to erosion and deposition. RS indices captured one fifth of the total variance explained, slightly changed the ranking of variance explained by the non-RS predictors, and reduced the variability of the model replicates. During the study period, SOC decreased in the areas with relatively high initial SOC, and increased in the area with high temperature and low rainfall, dominated by arables. This was likely due to the compulsory application of some Good Agricultural and Environmental practices. These results confirm that the importance of texture and land use in short-term SOC variation is comparable to climate. The present results call for agronomic and policy intervention at the district level to maintain fertility and yield potential. In addition, the present results suggest that the application of RS covariates enhanced the modelling performance.

Uncertainty in crop model predictions

Crop models are used to estimate crop productivity under future climate projections, and modellers manage uncertainty by considering different scenarios and GCMs, using a range of crop simulators. Five crop models and 20 users were arranged in a randomized block design with four replicates. Parameters for maize (well studied by modellers) and rapeseed (almost ignored) were calibrated. While all models were accurate for maize (RRMSE from 16.5% to 25.9%), they were, to some extent, unsuitable for rapeseed. Although differences between biomass simulated by the models were generally significant for rapeseed, they were significant only in 30% of the cases for maize. This could suggest that in case of models well suited to a crop, user subjectivity (which explained 14% of total variance in maize outputs) can hide differences in model algorithms and, consequently, the uncertainty due to parameterization should be better investigated. Five crop models and 20 users were arranged in four randomized blocks.The significance of model factor for maize and rapeseed was evaluated.All models achieved good performance for maize and poor for rapeseed.Differences between models were significant only in 30% of the cases for maize.Parameterization uncertainty should be explicitly managed also in model ensembles.