Stefano Bocchi

Multi-year monitoring of rice crop phenology through time series analysis of MODIS images

Precise phenological calendars, for each cultivated species and variety, are necessary both to highlight anomalous agronomic situations and to feed crop models. This study, conducted in the Italian rice area, focuses on the evaluation of the contribution of remote sensing satellite data to providing phenological information on rice cropping systems. A time series of 5 years (2001–2005) of the Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composites was analysed with the TIMESAT program in order to automatically retrieve key phenological information such as the start of season (emergence), peak (heading) and end of season (maturity). The procedure involved two steps: (1) interpolation and smoothing of MODIS NDVI temporal profile and (2) the analysis of a temporal signal for the extraction of the phenological metrics. The remote sensing estimates were evaluated using information regarding cultivated variety, sowing dates, management and production directly acquired from rice farmers. A good correlation (R 2 = 0.92, n = 24) has been observed between estimates derived from satellites and estimates produced with the traditional Growing Degree Days (GDD) method based on thermal unit accumulation. Improved estimates of the maturity stage were obtained using a procedure that integrates satellite and GDD methods; however its application requires spatially distributed information on the cultivated varieties. Satellite derived maps of the retrieved phenological parameters showed an intra-seasonal pattern related to different cultivated varieties. Inter-seasonal analysis allowed the anomalous behaviour of the year 2003 to be highlighted, characterized by rapid growth at the beginning of the spring and an early senescence. The results confirm the potential of remotely sensed data for the monitoring of crop status and for the forcing of crop models in a spatially distributed way.

Application of factorial kriging for mapping soil variation at field scale

Abstract Use of precision farming technologies requires better understanding of soil variability in physical, hydraulic and chemical properties. Some of that variation is natural, some is the result of the management history of the field. So, to match agricultural inputs and practices to site-specific conditions, the factorial kriging algorithm (FKA) was used to analyze spatial variability in some soil physical, hydraulic and chemical properties (sand and silt concentrations, water contents corresponding to potentials of −10, −50, −100, −200, −1000 and −1500 kPa and organic C concentration), measured at two depths within a single field in north Italy. A linear model of coregionalization, including, (1) a nugget effect; (2) an exponential structure with an effective range of 120 m and (3) an exponential structure with an effective range of 350 m, was fitted to the experimental direct and cross-variograms of the properties of top layer. Cokriged regionalized factors, related to short and long-range variation, were then mapped to characterize soil variation across the field. Short-range soil variation was produced essentially by differences in soil texture, whereas long-range variation in organic carbon concentration resulted in dishomogeneity of soil water retention. Quite probably, the variation in organic carbon concentration was caused by the patchy discharge of liquid manure made on the field. FKA, combining pedological expert knowledge with geostatistical techniques, could be very useful to farmers so that each area within a field is managed appropriately.

Parameterization of a crop growth and development simulation model at sub-model components level. An example for winter wheat (Triticum aestivum L.)

Abstract Dynamic simulation models are frequently used for assessing agronomic and environmental effects of different management practices, under various pedo-climatic conditions. CropSyst is a suitable cropping systems simulation model for such applications. However, available CropSyst crop parameters for winter wheat, one of the most important cereals in the world, are limited. In this work we show that it is possible to parameterize separate sub-model components by using existing experimental data and literature. The experiments, carried out in northern Italy between 1986 and 2001, quantified the dynamics of aboveground biomass (AGB), plant nitrogen (N) concentration (PNC) and N uptake (UPTK) by means of periodical measurements. The relative root mean square error (calculated by dividing the root mean square error by the average of observations) obtained after model calibration and validation on an independent data set was, respectively, in the range 9–30% and 17–32% for AGB, 10% and 6–40% for PNC, 8–28% and 9–24% for UPTK. AGB was frequently underestimated. Despite the limited accuracy of simulations, we argue that calibrated crop parameters are adequate for scenario analysis as most differences between years and fertilization levels were reproduced by the model and final AGB and cumulative UPTK were also correctly simulated.

Environmental profile of paddy rice cultivation with different straw management.

Italy is the most important European country in terms of paddy rice production. North Italian districts such as Vercelli, Pavia, Novara, and Milano are known as some of the worlds most advanced rice cultivation sites. In 2013 Italian rice cultivation represented about 50% of all European rice production by area, and paddy fields extended for over 216,000 ha. Cultivation of rice involves different agricultural activities which have environmental impacts mainly due to fossil fuels and agrochemical requirements as well as the methane emission associated with the fermentation of organic material in the flooded rice fields. In order to assess the environmental consequences of rice production in the District of Vercelli, the cultivation practices most frequently carried out were inventoried and evaluated. The general approach of this study was not only to gather the inventory data for rice production and quantify their environmental impacts, but also to identify the key environmental factors where special attention must be paid. Life Cycle Assessment methodology was applied in this study from a cradle-to-farm gate perspective. The environmental profile was analyzed in terms of seven different impact categories: climate change, ozone depletion, human toxicity, terrestrial acidification, freshwater eutrophication, marine eutrophication, and fossil depletion. Regarding straw management, two different scenarios (burial into the soil of the straw versus harvesting) were compared. The analysis showed that the environmental impact was mainly due to field emissions, the fuel consumption needed for the mechanization of field operations, and the drying of the paddy rice. The comparison between the two scenarios highlighted that the collection of the straw improves the environmental performance of rice production except that for freshwater eutrophication. To improve the environmental performance of rice production, solutions to save fossil fuel and reduce the emissions from fertilizers (leaching, volatilization) as well as methane emissions should be implemented.

Azolla-Anabaena as a Biofertilizer for Rice Paddy Fields in the Po Valley, a Temperate Rice Area in Northern Italy

Azolla is a floating pteridophyte, which contains as endosymbiont the nitrogen-fixing cyanobacterium Anabaena azollae (Nostocaceae family). Widely cultivated in the Asian regions, Azolla is either incorporated into the soil before rice transplanting or grown as a dual crop along with rice. To examine the feasibility of its use in flooded rice fields sited in the Temperate European Areas, we carried out a series of experiments in PVC tanks during 2000–2002 in Po Valley (northern Italy) conditions, to study the growth-development dynamics and the resistance/tolerance to low temperatures and to commonly used herbicides of several different Azolla strains. Three out of five strains tested survived the winter, with an increase in biomass from March to May producing approximately 30–40 kg ha−1 of nitrogen. One of these strains, named “Milan”, emerged as the most resistant to herbicide and the most productive. Of the herbicides tested, Propanil permitted the survival of growing Azolla.

Effects of cattle manure and components of pig slurry on maize growth and production

Abstract The aim of this project, carried out during the three-year period 1989–1991, was to assess the value of certain types of animal waste (cattle manure, slurry and solid matter from pigs) as fertilizer for maize. Using soil-filled tanks, a study was made of rates of application of slurry, administered in amounts to correspond to quantities of nitrogen (0, 180, 360 and 540 kg N ha−1), factorially combined with different rates of urea nitrogen (0, 90, 180 kg N ha−1). The various forms of organic waste were found to promote early ripening and increase plant size and grain yield. The highest rates of organic fertilizer produced the same yield as 180 kg N ha−1 as urea. Positive interactions between the combination of organic matter and urea N were observed. No cumulative negative effects were observed on the growth or productivity of the maize plants, even at the maximum rates of organic substance, repeated for three years in succession.

Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images

Recent studies using low-resolution satellite time series show that the Sahelian belt of West Africa is witnessing an increase in vegetation cover/biomass, called re-greening. However, detailed information on local processing and changes is rare or lacking. A multi-temporal set of Landsat images was used to produce land-cover maps for the years 2000 and 2007 in a semi-arid region of Niger, where an anomalous vegetation trend was previously detected. Several supervised classification approaches were tested: spectral classification of single Landsat data, temporal classification of normalized difference vegetation index time series from Landsat images, and two-step classification integrating both these approaches. The accuracy of the land-cover maps obtained ranges between 80% and 90% overall for the two-step classification approach. Comparison of the maps between the two years indicates a stable semi-arid region, where some change in hot spots exists despite a generally constant level of rainfall in the area during this period. In particular, the Dallol Bosso fossil valley highlights an increase in cultivated land, while a decrease in herbaceous vegetation was observed outside the valley where rangeland is the predominant natural landscape.

Spatial interpolation of soil physical properties for irrigation planning. A simulation study in northern Italy

Abstract To calculate water balances at a regional scale, a frequently adopted approach (choropleth mapping) consists of using soil profile observations to identify ‘homogeneous areas’, to which simulation models are applied. However, spatial variability of soil properties within ‘homogeneous areas’ is a potential source of error, if the relationship between model inputs and model outputs is not linear. The aim of this work is to assess the feasibility of using spatially variable soil information for providing more detailed inputs to simulation models and to evaluate its effects on calculated irrigation water requirements. Point observations of soil properties in the topsoil layer were collected in a plain area near Milano (northern Italy). Particle size distribution was determined on 154 samples. The cropping systems simulation model cropsyst was applied at the study area by using four different sets of soil input data: the first one was derived from the soil map (1 datum per soil mapping unit), the other three were obtained by the use of geostatistical procedures applied to point observations (several data per soil mapping unit). The results of cropsyst s multi-year simulation for grain-maize were used to calculate the amount of grain biomass produced, actual crop evapotranspiration (ET), irrigation water needed and soil water drainage (SWD) for each soil unit (SU), their standard deviation (S.D.) in time and their S.D. in space within each SU. A clear spatial structure could be identified for all georeferenced model inputs and for model outputs related to crop growth (yield, ET). Simulated values for grain yield (GY), actual ET, irrigation water applied (IWA) and SWD were very similar for choropleth mapping and for geostatistics-based procedures. The S.D. in time was low for variables related to crop growth and was increasing for IWA and SWD. For all simulated variables the S.D. in space was always very low. In general, the spatial variability of model results was much lower than the spatial variability of model inputs: this smoothing effect was due to the application of kriging, pedotransfer functions (PTF) and simulation modeling. These results suggest that for evaluating water management scenarios at this scale, when hydrological properties are not measured, georeferenced soil data are available only for topsoil, and variability of soil particle distribution within SUs is not too high, the choropleth mapping method can be successfully used.

Operational Monitoring of Daily Crop Water Requirements at the Regional Scale with Time Series of Satellite Data

This work presents a simple, cost-effective, and operational approach to monitor crop water requirements at the regional scale for water management and monitoring purposes. The recommended Food and Agricultural Organization of the United Nations methodology (FAO-56) calculates crop evapotranspiration using crop-specific coefficients ( Kc ) , which vary according to the crop type, health, and phenological stage. This approach, though widely applied for irrigation planning, cannot always match the appropriate crop coefficient with the actual crop phenological stage and health condition, especially in anomalous situations. Previous research demonstrated that crop coefficients and spectral vegetation indexes are correlated. Recent studies have used this relationship with high-resolution satellite data from different sensors to provide information to irrigation advisory services. However, high-resolution data are not feasible for an operational and routine monitoring of water consumption and needs. This paper ...

Evaluation of anaerobic degradation, biogas and digestate production of cereal silages using nylon-bags

In this study, the degradation efficiency and the biogas and digestate production during anaerobic digestion were evaluated for the cereal silages most used to feed biogas plants. To this purpose, silages of: maize from the whole plant, maize from the ear, triticale and wheat were digested, inside of nylon bags, in laboratory scale digesters, for 75days. Overall, the test involved 288 nylon bags. After 75days of digestion, the maize ear silage shows the highest degradation efficiency (about 98%) while wheat silage the lowest (about 83%). The biogas production ranges from 438 to 852Nm(3)/t of dry matter for wheat and ear maize silage, respectively. For all the cereal silages, the degradation as well as the biogas production are faster at the beginning of the digestion time. Digestate mass, expressed as percentage of the fresh matter, ranges from 38% to 84% for wheat and maize ear silage, respectively.