Characterization, Delineation and Visualization of Agro- Ecozones Using Multivariate Geographical Clustering

Self Cite

Cropping systems are affected by climate change because of the strong relationship between crop development, growth, yield, CO2 atmospheric concentration and climate conditions. The increasing temperatures and the reduction of available water resources may result in negative impacts on the agricultural activity in Mediterranean environments than other areas. In this study the CERES-Wheat and CROPGRO-Tomato models were used to assess the effects of climate change on winter wheat (Triticum durum L.) and processing tomato (Lycopersicon aesculentum Mill.) in one of most productive areas of Italy, located in the northern part of the Puglia region. In particular we have compared three different General Circulation Models (HadCM3, CCSM3, ECHAM5) subjected to a statistical downscaling under two future IPCC scenarios (B1 and A2). The analysis was carried out at regional scale repeating the simulations for seven homogeneous area characterizing the spatial variability of the region. In the second part of the study, considering only HadCM3 data set, climate change impact on long-term sequences of the two crops combined in three crop rotations were evaluated in terms of yield performances and soil fertility as indicated by the soil organic content of carbon and nitrogen. The comparison between GCMs showed no significant differences for winter durum wheat yield, while noticeable differences were found for yield and irrigation requirements of tomato. Under future scenarios, the production levels were reduced for tomato, whereas positive yield effects were observed for winter durum wheat. For winter durum wheat the simulation indicated that two- and three-year rotations, including one year of tomato cultivation, improved the cereal yield and this positive effect maintained its validity also in future scenarios. For both crops higher requirements of water and nitrogen were predicted under future scenarios. This result coupled with the decrease of yield caused negative reduction of water use efficiency and nitrogen use efficiency for tomato cultivation

Section: Study Areamentioning

confidence: 99%

Climate change impact on crop rotations of winter durum wheat and tomato in southern Italy: yield analysis and soil fertility

Ventrella¹,

Giglio²,

Charfeddine³

et al. 2012

Self Cite

“…The downscaling procedure for P based on monthly frequencies did not produce good results (data not shown). The short length of the climate time series and its large variability related to the geographical area (Castrignanò et al, 2010;Vitale et al, 2010) may likely be the cause. In order to improve the downscaling results, we computed the winter (December, January, February; DJF), the spring (March, April, May; MAM), the summer (June, July, August; JJA) and the autumn (September, October, November SON); seasonal values.…”

Section: Resultsmentioning

confidence: 99%

The water deficit and aridity indexes in the Capitanata plain calculated by statistical downscaling

Palatella

Vitale²,

Ventrella³

et al. 2012

This work describes the results obtained by the statistical downscaling technique for the assessment of changes in precipitation (P), potential evaporation (PE). In turn P and PE are used for computing two indexes of water availability, namely the index of water deficit (WDI) and the aridity index (AI). The analysis is carried out for the Capitanata plain (South-East of Italy) and the A2 scenario of the IPCC Assessment Report 4 (AR4). The large-scale temperature at the 1000hPa level and sea level pressure fields are used as predictors. The local precipitation and potential evaporation time series are used as predictands. The statistical downscaling technique used is based on Canonical Correlation Analysis. A validation procedure of the model is performed and the same technique is used for climatic projections of P, PE and consequently WDI and AI. Climate analysis and projections at this local space scale is an important issue not only for current water management and planning, but also for improving the irrigation efficiency considering future climate change scenarios.

“…Soil characterization has been carried out applying a combined approach, linking multivariate geostatistical technique of cokriging with an algorithm of clustering, based on nonparametric density estimate, according to which a cluster is defined as a region surrounding a local maximum of the multivariate probability density function (Rinaldi et al, 2007a). Pedological and soil organic matter data of the rooted topsoil (0-0.40 m depth), textural and hydrological parameters, were all submitted to a normalizing transformation, and then analyzed by the multivariate geostatistical technique of cokriging (Castrignanò et al, 2010). The application of the clustering approach to the (co)kriged variables, produced the subdivision of the land into 8 distinct classes.…”

Section: The Areas Of the Case Studymentioning

confidence: 99%

AQUATER Software as a DSS for Irrigation Management in Semi-Arid Mediterranean Areas

Acutis¹,

Perego²,

Bernardoni³

et al. 2010

Irrigation management at district or regional scale can be dealt using ecological process-based models and remote sensing data. Simulation crop models simulate at a certain time step the main biophysical variables determining crop photosynthesis and water consumption rates. The research consists in an integrated approach to combine field data, simulation crop model and remote sensing information. Detailed data sets related to topography, soil, climate and land cover were collected and organized into a Geographic Information System, which is routinely updated with remotely sensed images. The code implementation of these two models allows for an improvement of simulation reliability for the crop types considered in the present study in Mediterranean area. Remote sensing images detected by optical and radar satellite sensors at different spatial scales (from 10 to 50 m) have been collected over the analyzed crop cycles. Therefore, remote sensing information about land use and leaf area index (LAI) are assimilated dynamically by the model, to increase the effectiveness of simulation. The integration of crop and water dynamics models with the updated remote sensing information is a Decision Support Systems, AQUATER software, able to integrate remote sensing images, to estimate crop and soil variables related to drought, and subsequently to assimilate these variables into a simulation model at district scale. The significant final outputs are estimated values of evapotranspiration, plant water status and drought indicators. The present work describes the structure of AQUATER software and reports some application results over 2006 and 2007 cropping seasons in Capitanata, South-East Italy. This region has been divided in simulation units cropped by tomato (Lycopersicon lycopersicum L.), sugar beet (Beta vulgaris L. var. saccharifera) and durum wheat (Triticum durum Desf.). Two types of comparison have been carried out: (i) between some tomato observed and simulated data, and (ii) between "LAI Forcing" and "No LAI Forcing" simulated data. LAI Forcing data have been detected by remote sensing over the crop cycle and over the whole region. The model showed a relevant coherence between observed and simulated data (RRMSE = 32, 55, 30% for above ground biomass, LAI and soil water content, respectively). In the case of the application of the LAI Forcing procedure, since simulated LAI are lower than the observed values, as a consequence, simulation results underestimate.