Agricultural Internet of Things and decision support for precision smart farming

Agricultural Internet of Things and Decision Support for Smart Farming reveals how a set of key enabling technologies (KET) related to agronomic management, remote and proximal sensing, data mining, decision-making and automation can be efficiently integrated in one system. Chapters cover how KETs enable real-time monitoring of soil conditions, determine real-time, site-specific requirements of crop systems, help develop a decision support system (DSS) aimed at maximizing the efficient use of resources, and provide planning for agronomic inputs differentiated in time and space. This book is ideal for researchers, academics, post-graduate students and practitioners who want to embrace new agricultural technologies

Insights into the Effects of Study Area Size and Soil Sampling Density in the Prediction of Soil Organic Carbon by Vis-NIR Diffuse Reflectance Spectroscopy in Two Forest Areas

Sustainable forest land management requires measuring and monitoring soil organic carbon. Visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR, 350–2500 nm), although it has become an important method for predicting soil organic carbon (SOC), requires further studies and methods of analysis to realize its full potential. This study aimed to determine if the size of the study area and soil sampling density may affect the performance of Vis-NIR diffuse reflectance spectroscopy in the prediction of soil organic carbon. Two forest sites in the Calabria region (southern Italy), which differ in terms of area and soil sampling density, were used. The first one was Bonis catchment area (139 ha) with a cover consisting mainly of Calabrian pine, while the second was Mongiana forest area (33.2 ha) within the “Marchesale” Biogenetic Nature Reserve, which is covered by beech. The two study areas are relatively homogeneous regarding parent material and soil type, while they have very different soil sampling density. In particular, Bonis catchment has a lower sampling density (135 samples out of 139 ha) than Mongiana area (231 samples out of 33.2 ha). Three multivariate calibration methods (principal component regression (PCR), partial least square regression (PLSR), and support vector machine regression (SVMR)) were combined with different pretreatment techniques of diffuse reflectance spectra (absorbance, ABS, standard normal variate, SNV, and Savitzky–Golay filtering with first derivative (SG 1st D). All soil samples (0–20 cm) were analyzed in the laboratory for SOC concentration and for measurements of diffuse reflectance spectra in the Vis-NIR region. The set of samples from each study area was randomly divided into a calibration set (70%) and a validation set (30%). The assessment of the goodness for the different calibration models and the following SOC predictions using the validation sets was based on three parameters: the coefficient of determination (R2), the root mean square error (RMSE), and the interquartile range (RPIQ). The results showed that for the two study areas, different levels of goodness of the prediction models depended both on the type of pretreatment and the multivariate method used. Overall, the prediction models obtained with PLSR and SVMR performed better than those of PCR. The best performance was obtained with the SVMR method combined with ABS + SNV + SG 1st D pretreatment (R2 ≥ 0.77 and RPIQ > 2.30). However, there is no result that can absolutely provide definitive indications of either the effects of the study area size and soil sampling density in the prediction of SOC by vis-NIR spectroscopy, but this study fostered the need for future investigations in areas and datasets of different sizes from those in this study and including also different soil landscapes

Vis-NIR Spectroscopy for Determining Physical and Chemical Soil Properties: An Application to an Area of Southern Italy

The development of rapid, accurate, cost effective methods to determine soil physical and chemical properties is important for sustainable land management. In the last two to three decades, the interest in using visible and near infrared (Vis-NIR) spectroscopy as an alternative method for determining soil properties has increased. To obtain reliable predictions of soil properties, multivariate calibration techniques such as Partial Least Squares Regression (PLSR) are commonly used to correlate the spectra with the chemical, physical and mineralogical properties of soils.The objective of the paper was to assess the potential of Vis-NIR spectroscopy coupled with PLSR to determine soil chemical and physical properties such as organic carbon (SOC), sand, silt, clay, and calcium carbonate (CaCO3) contents in a sample site of southern Italy.Spectral curves showed that the soils could be spectrally separable on the basis of chemical and physical properties. PLSR calibration models were derived for each of the soil properties and were validated with an independent data set. The optimum number of factors to be retained in the calibration models was determined by leave-one-out cross-validation. The accuracy of the calibration and validation models for the different soil properties was evaluated with the coefficient of determination (R2) and the root mean squared error (RMSE). The results showed that predictions were satisfactory for all soil properties analyzed with high values of R2 > 80.A combination of Vis-NIR spectroscopy and multivariate statistical techniques, therefore, can be used as a rapid, low cost and quantitative means of characterizing the soils of southern Italy

Drought events at different timescales in southern Italy (Calabria)

<div><p>This study reports an analysis of the spatial drought patterns for a region of southern Italy (Calabria) based on a homogenous monthly precipitation data set of 129 rain gauges for the period 1916–2006. Drought was expressed using the Standardized Precipitation Index (SPI), and drought events were analyzed using both the short-time (3 and 6 months) and the long-time (12 and 24 months) SPI. In particular, in order to characterize the SPI spatial pattern, index data of the three most severe drought events were interpolated and mapped using a geostatistical approach. Results show that these heavy drought episodes have widely affected the Calabria region and the drought that occurred in 2002 was the worst in terms of spatial extent both at short- and long-time scales.</p></div

Organic carbon and total nitrogen topsoil stocks, biogenetic natural reserve ‘Marchesale’ (Calabria region, southern Italy)

It is essential estimating the spatial distribution of soil organic carbon (SOC) and soil total nitrogen (STN) stocks and their spatial-temporal variations to understand the role of soil in ecosystem services and in the global cycles of carbon and nitrogen. This work was aimed to quantify and map the stocks of SOC and STN in topsoils in an area of the Biogenetic Natural Reserve ‘Marchesale’ (Calabria region, southern Italy). Forest soil samples (0–20 cm depth) were collected at 231 locations and analysed in laboratory for SOC and STN. Moreover, in all samples, bulk density (BD) and soil coarse fragments (SCFs) were determined. Geostatistics was used to map all soil properties (SOC, STN, BD and SCFs) and the stocks of SOC and STN. The mean stock values were 86.3 Mg ha−1 for SOC and 5.1 Mg ha−1 for STN. The total amounts stored in the study area (33.2 ha) were 2865.2 Mg for SOC and 170.1 Mg for STN. Although only the topsoil was considered, the accompanying maps (1:4000 scale) will be useful for the sustainable management of the Biogenetic Natural Reserve ‘Marchesale’ and for undertaking appropriate conservation plans to mitigate the emissions of greenhouse gases

Assessment and Mapping of Soil Salinization Risk in an Egyptian Field Using a Probabilistic Approach

The assessment of soil salinization risk at the field scale requires modeling of the spatial variability of soil salinity. This paper presents a probabilistic approach to estimate and map a risk index using all available auxiliary information. A probabilistic methodology is proposed to estimate the conditional probability of exceeding the assigned threshold value of a generic indicator of soil salinity. A geostatistical non-parametric technique, probability kriging, was used to assess the risk of soil salinization and delineate different hazard zones within a field. The technique relies on indicator coding of information. The approach was applied to soil electrical conductivity measurements collected in an experimental field located in the Nile Delta region in Egypt, and submitted over time to trials with different fertilization treatments. The application of the method allowed delineation of a north-eastern zone in the field with a high risk of soil salinization due to its lack of cultivation for a long time and nearness to buildings that prevent water infiltration. The method proved to be quite promising from the perspective of precision agriculture and it is easily extendable to any sort of remote and proximal sensing auxiliary information, including information on the deepest layers of soil

Impatti ambientali - PROGETTO ALGENCAL Sistema innovativo per la coltura di microalghe in fotobioreattori per la produzione di energia da fonte rinnovabile e di altri prodotti

Per limitare l’utilizzo di carburanti derivanti dal petrolio, che contribuiscono al riscaldamento globale e sono disponibili in numero limitato, è necessario studiare ed individuare fonti alternative di approvvigionamento, green e rinnovabili, da destinare alla produzione di biocarburanti. Lo studio effettuato nel presente lavoro ha aperto una nuova strada verso la produzione di microalghe all’interno di un impianto dimostrativo su scala pseudo-industriale da destinare alla produzione di bio-olio e altri output finali quali energia e prodotti a maggiore valore aggiunto. Le microalghe rappresentano una valida alternativa non solo alla produzione di carburanti di origine fossile, ma anche ai biocarburanti di prima generazione che utilizzano piante superiori prodotte tramite i normali metodi di coltivazione. Però, al momento non esistono processi produttivi in grado di fornire quantitativi di biodiesel adeguati ad una scala produttiva elevata e che garantiscano un bilancio positivo dal punto di vista economico ed energetico. I punti critici sono rappresentati soprattutto dai processi per la separazione della biomassa microalgale dal mezzo acqueo di coltura e dall’estrazione dell’olio contenuto nelle microalghe, ancora molto costosi per grandi quantitativi. Il sistema studiato ha tentato di superare queste criticità, focalizzandosi sull’individuazione di un metodo semplice da utilizzare, con il minimo impiego di risorse energetiche e con un’alta riproducibilità ed esportabilità. Quest’ultimo aspetto diventa fondamentale per poter replicare le metodologie di coltivazione studiate anche in altre realtà, soprattutto quelle del bacino del Mediterraneo che presentano caratteristiche climatiche ed ambientali molto simili al sito di installazione dell’impianto prototipale. I risultati raggiunti sono stati soddisfacenti; le soluzioni individuate, seppure rispondono positivamente in particolare ai problemi di dewatering e di estrazione degli olii, per poter essere effettivamente implementate richiedono ancora un ulteriore step di ottimizzazione

Spatial and temporal distribution of precipitation in a Mediterranean area (southern Italy)

The precipitation climate regime of a region is characterized by the distribution of the monthly precipitation contribution. Its temporal and spatial analysis is particularly interesting for many fields of applied sciences, such as climatology, hydrology and water resources management. With the aim to describe the climate regime, its spatial feature and relevant potential temporal shift, for a large area of southern Italy (Mediterranean basin), a database of about 559 stations has been explored through the statistical analysis of rainfall time series spanning between 1917 and 2006. After a change point analysis, aimed at the assessment of data quality, a trend analysis has been performed on both monthly precipitation, monthly percentage of annual rainfall amount and PCI-computed series. The broad extension of the area under investigation highlights a better understanding of precipitation distribution patterns over space. Results of PCI trend analysis show a significant shift, for about 40-50 % of total gauging station, over the time towards a more uniform climate regime, especially for the hilly areas. Moreover, the trend analysis on the monthly rainfall series indicates that the shift is produced by a reduction of rainfall amount during the winter season, particular consistent over the Tyrrhenian side of the peninsula, and an increase during the summer season quite widespread over the whole investigated territory