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Increasing the organic matter content of the soil has been presented in the:”4per1000″ proposal as a significant climate mitigation measure able to support the achievement of Sustainable Development Goal 13 - Climate Action of United Nations.At the same time, the report of the Mission Board for Soil health and Food, "Caring for soil is caring for life," indicates that one of the targets that must be reached by 2030 is the conservation and increase of soil organic carbon stock. De facto, the panel clearly indicates the soil organic carbon as one of the indicators that can be used to monitor soil health, and at the same time, if the current soil use is sustainable or not.Thus it is to be expected that the monitoring of SOC will become requested to check and monitor the sustainability of agricultural practices realized in the agricultural areas. For all the above reasons, the development of a reliable and fast indirect methods to evaluate the SOC is necessary to support different stakeholders (government, municipality, farmer) to monitor SOC at different spatial scales (national, regional, local).Over the past two decades, data mining approaches in spatial modeling of soil organic carbon using machine learning techniques and artificial neural network (ANN) to investigate the amount of carbon in the soil using remote sensing data has been widely considered. Accordingly, this study aims to design an accurate and robust neural network model to estimate the soil organic carbon using the data-based field-portable spectrometer and laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The measurements will be on two sets of the same soil samples, the first by the standard protocol of requested laboratories for soil scanning, The second set of the soil samples without any cultivation to simulate the soil condition in the sampling field emphasizes the predictive capabilities to achieve fast, cheap and accurate soil status. Carbon soil parameter will determine using, multivariate regression method used for prediction with Least absolute shrinkage and selection operator regression (Lasso) in interval way (high, medium, and low). The results will increase accuracy, precision, and cost-effectiveness over traditional ex-situ methods.

POSITIVE (Scalable Operational Protocols for precision agriculture) is a precision agriculture project for variable rate irrigation designed to improve the functionality of the IRRIFRAME system, the irrigation advice service of the Emilia-Romagna region. POSITIVE is based on satellite remote sensing, the use of vegetation indices for crops, IoT (Internet of Things) technologies, Big Data and 4.0 irrigation machinery. A central server manages the information flows and provides variable rate irrigation maps for farmers as final users. The system is public and free. In the first year of "IRRIFRAME plus" system experimentation (improved version of IRRIFRAME through POSITIVE machinery), at the experimental farm located in Mezzolara di Budrio (BO - Italy), promising results have been obtained for maize, with a WUE (Water Use Efficiency) going from 4.2 g l-1 (with standard IRRIFRAME service) to 5.2 g l-1. For sparse crops, such as tomato and onion, results were not so satisfactory. The future years of experimentation will allow to improve the calibration of VI-crop coefficient (Kc) correlation in order to improve the response in condition of partial soil cover and wetting condition.

The case study of Tilmen Hoyuk is presented here to describe a combination of different integrated approaches. The site has been assessed from a climatic, topographical, pedological, hydrological, vegetational and vegetation management standpoint. This study provided a wealth of information that was used for acquiring a deeper understanding of the present environment, in view also of designing and planning the archaeological park. At the same time, remote monitoring was experimented in view of future applications and management protocols.

As a result of climate change, causing high temperature, erratic precipitation, and extreme meteorological events, in recent times in Italy productivity of Maize is becoming less reliable. Climate change effects are accompanied by the increase in the presence of mycotoxins and various pathogens, which contribute to the reduction of the possibility of successfully producing Maize. In this framework, Proso Millet (Panicum miliaceum L.) may be an interesting alternative, as it is a relatively low-demanding crop, highly drought-resistant, and can be employed, similarly to Sorghum, in rotation, maintaining a certain amount of biodiversity and contributing to the revenue for the farmers. Moreover, Proso Millet has a very short cycle, and may be used as a catch crop, when other crops have failed or after their harvest. Millet used to be cultivated in ancient times in Italy, but then it was abandoned in favor of Maize, so now it is necessary to re-define proper agricultural practices and managements, as well as to remedy to the lack of an exact description of its phenological development. In the frame of a Life-CCA EU project, called Growing REsilience AgriculTure—Life (GREAT LIFE), aim of this work is to encode phenology of Proso Millet using BBCH scale. The lack of an exact definition of Proso Millet phenology is a major drawback in progressing in research on this crop, which could be a very valuable tool for improving the resilience of agro-ecosystems to climate change in the Mediterranean basin. For this purpose, Proso Millet was cultivated in two experimental sites in the Emilia-Romagna region (North of Italy). The crop was closely monitored throughout the life cycle, in order to document, even photographically, the achievement of the subsequent phenological phases (including the time necessary to reach each phenological stage, expressed as Days After Sowing—DAS). Thanks to weather data collection from agrometeorological stations close to the experimental fields, it was possible to correlate the phenological development to temperature-driven heat-unit accumulation (Cumulated Growing Degree Days—CGDD), using the single triangle method (useful tool for forecasting purposes). Ancillary agronomic data have also been collected, for completeness. This study well describes primary and secondary phenological stages of Proso Millet, managing at encoding them in the BBCH scale and contextually providing DAS and CGDD values necessary to achieve the different phenophases. The difference observed between the two experimental sites in reaching each BBCH stage according to both CGDD and DAS is mostly restrained, suggesting that this work may represent a valid first tool in defining the phenological development of Proso Millet in the areas of Northern Italy. The effort made to encode Proso Millet phenology in BBCH scale may be useful to give to researchers comprehensive indications for future agronomic surveys on the crop. The agronomic data collected show that the crop had a good agronomic performance despite the adverse weather pattern during the season, enlightening for farmers the opportunity offered by Millet in Italy as a resilient crop.

ALADIN (Agroalimentare Idrointelligente) is a two-year project dedicated to precision irrigation. It integrates different systems for collecting information (monitoring of crop conditions), processing of responses (irrigation prescription maps) and implementation in the field (variable rate irrigating machine). The experimental protocol is tested in four different farms in the Emilia Romagna region on two important crops, maize and tomato. The NDVI index, positively correlated to the crop vigor condition, is used as an indicator of the plants water status. The IRRIFRAME irrigation service, based on integration of NDVI information, produces a prescription map to be supplied to the variable rate irrigating device.

BACKGROUND: A likely increasing demand for varieties mixtures, landraces and genetic diversity in cropping systems will underpin calls for models able to generalize phenological development at the species level, at the same time as providing the expected range of phenological variability. In the present article, we aimed to obtain a generalized phenological model of durum wheat (Triticum durum, Desf.). RESULTS: Using a large phenological dataset embracing field data collected under different sowing dates, varieties and locations over the Italian peninsula, we searched for the phenophases enabling the best linear approximations between developmental rates and air temperature, aiming to minimize the residual variability from drivers other than temperature, as genetic and environmental diversity. The developmental rates of the resulting phases were then examined with respect to the mean daylength to determine possible additional relations with photoperiod. If a correlation with daylength was also present, the developmental rate is calibrated by multiple linear regression, or otherwise by simple linear regression of temperature. The resulting calibration, tested on an independent data subset, confirms that the model is able to generalize wheat development over the Italian peninsula with high accuracy (mean absolute error =3–8 days; r2 = 0.75–0.98), regardless of the wheat variety. CONCLUSION: The generalized phenological model is potentially suitable for many agro-ecological and large-scale applications. It is hoped that the model will aid in situations where phenological observations to parameterize a model are still lacking, as is probably the case for landraces and underutilized crop varieties. © 2019 Society of Chemical Industry.

ALADIN was a two-year project dedicated to precision irrigation. It integrates different systems for collecting information (monitoring of crop conditions in site, from drone and satellite), processing of responses (irrigation prescription maps) and implementation in the field (variable rate irrigating machine). The experimental protocol was tested in two different farms in the Emilia Romagna region on two important crops, maize and tomato. Several indices from UAV were calculated, starting from single bands, and together with the NDVI index from UAV and Satellite, correlated to the crop vigour condition, and plants water status. Results will be used to produce prescription maps for precision irrigation.

Phenological field observations were used to parameterize and test a Degree Day model for durum wheat (Triticum durum, Desf.) across the Italian peninsula. Data come from different experimental fields located in northern, central and southern Italy and encompass scalar sowing dates, allowing to parameterize a model representative for a wide range of sites and growing seasons. To assure the basic assumption of the Degree Day method, wheat subphases were identified to enable the best linear relationship (less data dispersion and higher coefficient of determination) between developmental rates and average air temperature. Under proven linear temperature responses, the model gave satisfactory simulations of wheat phenology over different locations and sowing period (R2 = 0.96; RMSE = 8,4 days; no bias, minimal complexity), even if the heat supply (growing degree days counted from 0°C) was very different among sites.

The paper presents the result of an application of the GeoWEPP model in a heterogeneous semi-agricultural catchment located in the northern Italian Apennines mountain range. The objectives were: (a) to evaluate the GeoWEPP model in a heterogeneous catchment in a Mediterranean climate and (b) to examine the effect of digital elevation model grid size on hydrological and sediment yield simulations. The catchment is characterized by large heterogeneity in geology, soil type, vegetation cover and topography. In addition, 10% of its area is occupied by calanchi (badlands), characterized by steep, bare soil and accentuated erosion. Experimental streamflow data were compared with those simulated by GeoWEPP for a period of eight years and the results were evaluated by means of statistical indices, with the analysis of the flow duration curve. Simulated sediment yields were compared with experimental data for one year. The streamflow cumulative annual results were satisfactory with NSE oscillating between 0.40 and 0.83 and RMSE between 1.1 and 2.9 mm. Also, the performance of the model with daily streamflow data was positive (NSE = 0.68 and RMSE = 1.9 mm). The flow duration curve indicated that GeoWEPP could represent the experimental streamflow for fluxes over 1 mm d−1. The model performance for simulation of sediment yield was satisfactory with both digital elevation models of different grid sizes (NSE = 0.84 and 0.87). Indeed, the sensitivity analysis tests of the model showed that there was no statistically significant improvement in the accuracy of the digital elevation model between 10 and 2 m resolution. These results were confirmed for both streamflow as well as sediment yield. Additional sensitivity analysis of other model parameters performed on the entire catchment and badlands hillslopes showed that bedrock hydraulic conductivity primarily affected the model in both settings. Copyright © 2014 John Wiley & Sons, Ltd.

The paper presents a Precaution Index (P) developed to create an instrument to assess the potential risk related to the presence of pollution sources next to milk production farms. The methodology starts from the inventory and geo-referencing of all pollution sources in an area, with the help of the multi-criteria decision analysis. Results are represented in a map of P, which highlights which areas are most at risk and need monitoring, and which pollutants may be involved. This may help in the early identification of emerging hazards to food safety. P was calculated for the milk production in an Italian region. Results show that 97% of the area is in a condition of minimum precaution that would be needed in controlling the safety of dairy products.

The study reports an integrated environmental quality monitoring of a 100 km2 area in central Italy mostly occupied by an underground station of methane storage, working since 1982. The nitrogen oxides, ozone and isoprene concentration detached with a network monitoring of passive filters were compared with the results of lichens biomonitoring. Data from the two monitorings were in accordance: there was an inversely correlation between lichen biodiversity index (IBL) and NOx (−0.96) and ozone (−0.80), and a positive correlation between IBL and isoprene (0.67). IBL indicated that the area ranged between medium naturalness and medium alteration status, values fully compatible with the medium–high level of eutrophication, caused by intensive agriculture. Only two areas were in high alteration status, due to their proximity to glass factories and to a quarries area. Despite almost thirty years of activity, the environment quality of the area around the station did not show signs of declining.

Agriculture has both direct and indirect effects on quality of surface water and is one of the key activities causing water quality degradation. Its environmental impact can be evaluated by the determination of indicators of the quality of water bodies that collect drainage and runoff waters from agricultural watersheds. For this research, the water quality draining from three watersheds, totally or partially cultivated, all within the Po river valley (Italy), was determined, using chemical indicators (N-NO3 and N-NH4 concentration, N balance), trophic status (chlorophyll-a concentration) and benthic population indexes. Together, they should provide an overview of the water status, which is supposed to be strictly related to the land use and the management. Results show that the chemical parameters are well related to land use and farming management: intensive agricultural activity leads to high N-NO3 concentration in water and N surplus and vice versa. The chlorophyll-a concentration follows the same trend, being linked to nitrogen loads and land use. Not always there is accordance between chemical and biological indicators: no direct correspondence is evident between the N-NO3 concentration in waters and benthic community. Its presence and abundance seems to be mostly correlated with the geomorphology, hydrology, riparian strips, etc. of the habitat than to the land use. Only the integration of chemical and biological parameters allows a correct understanding of the state of health of water body and benthic communities.

The research was carried out for 7 years, 1998-2005, in a semi-agricultural watershed, called Centonara, set within a natural regional park and situated in the hills surrounding Bologna, northern Italy. This area is characterized by one of the most interesting badlands complexes in Europe and represents one of the main points of naturalistic interest. The watershed is partially cultivated (about 30% of the total area) with arable crops, mostly cereals and alfalfa. To evaluate the impact of agricultural activity on the eco-sustainability of this area, the nitrogen (N) balance was computed. Although it is only an estimation of the potential environmental damage, the nitrogen balance is a useful indicator of the risk posed to the environment from excessive nitrogen and can be useful to understand the possible effects of a certain type of agricultural and environmental management and policy. The balance was calculated by computing the difference between all inputs and all outputs. The nitrogen balance of the watershed was found to be sustainable, with an annual nitrogen balance ranging between –2.3 and +4.4 kg ha–1. Despite the limited presence of arable lands, the agricultural management played the main role in determining the sustainability of the watershed, strongly influencing both the principal N sources and sinks. In fact, major N inputs derived from inorganic fertilization (8.1-15.5 kg ha–1yr–1) and biological fixation (8.3-14.3 kg ha–1yr–1). On the other hand, plant removal constituted the most important output (17.7-25.6 kg ha–1yr–1). N losses in the drainage water were limited (3.0-9.5 kg ha–1yr–1) and the Centonara stream water was found to be unpolluted, with a nitrate concentration always below the EU limit for drinking water. The similar magnitude of total N inputs and outputs indicated that the crop management, especially the crop rotation and the N fertilization, in the Centonara watershed has reached a good level of ecological sustainability. Finally, the computation of the N fertilizer-use efficiency index resulted to be useful to identify which crop and which type of management (organic or conventional) were more suitable for the pedo-climatic condition of the studied area.

In the present global situation, agriculture plays a major role in the interaction between socio-economic and biophysical processes. In addition to its principal and fundamental role of providing food, it now also needs to consider other ecosystem services provided by agriculture and to explore the new frontiers for the the future. In the 50’s of the 20th century the major topic was the introduction of inorganic fertilizers, in the 60’s the use of synthetic compounds for plant protection (insecticides, herbicides, fungicides), in the 70’s industrial crops, in the 80’s organic farming and the environmental impact of agronomic practices, and in the 90’s genetically modified crops (herbicide tolerance, insect resistance). In the current decade the themes are: land and water degradation, the production of agricultural biomass for bio-energy, and the increased expression of functional compounds in crops. The Bologna X Congress of ESA “Multi-functional Agriculture - Agriculture as a Resource for Energy and Environmental Preservation”, will meet the needs of finding tools to deal with environmental problems coupled with the increasing demand for food, and filling the knowledge gap on the physiological relationships between functional compound bio-synthesis and agricultural practices. Members of the European Society for Agronomy already have a deep knowledge of these issues, and the Bologna ESA Congress will provide an opportunity to develop them further particularly in regard to innovative agricultural techniques, new energy sources and better environmental monitoring.

The Water Erosion Prediction Project (WEPP) model was tested using data from a detailed study conducted on experimental plots in the Apennines Mountain Range, northern Italy. Runoff, soil water and sediment data, together with weather information, were collected on an hourly basis at the study site. WEPP was first applied to simulate transient surface runoff, soil water and erosion. Two important input parameters, the biomass energy ratio for crop and the effective hydraulic conductivity of surface soil, were calibrated using field-observed runoff, soil water, erosion and plant biomass data. The calibrated model was then used to simulate the hydrologic and erosion impacts of three typical crop rotations, thereby to evaluate their abilities in reducing surface runoff and sediment yield. Results indicated that, with the definition of a restrictive layer at the bottom of the soil profile and the calibration of the two crucial model parameters, WEPP could adequately account for the water balance for the modeled experimental plot. For the study area, continuous corn with a conservation practice that delayed primary and secondary tillages produced low surface runoff and soil erosion, from both field observation and WEPP modeling. However, this mono-cultural practice may lead to accelerated soil-quality degradation. On the other hand, a four-year-rotation, corn–wheat–alfalfa–alfalfa, was predicted to substantially reduce soil erosion and has potential to become a sustainable cropping system under the pedo-climatic settings of the study area.

Il progetto nasce dalla collaborazione tra Filiera AQ di Granarolo, che svolge la sua attività industriale nel settore lattiero caseario, e il dipartimento di Scienze Agrarie dell'Università di Bologna allo scopo di reperire e georeferenziare informazioni sul rischio ambientale e creazione di indici di rischio. La ricerca ha messo in evidenza che al momento attuale le aziende produttrici di latte conferenti a Filiera AQ risultano in maggioranza distribuite in aree a riscio potenziale trascurabile o minimo, sufficientemente lontane da importanti agglomerati industriali.

The quality of agricultural products may be influenced by the environmental pollution. The prevention of pollution risk, or at least its knowledge, can be supported by the monitoring of the pollutions sources of potential environmental risk (e.g., incinerators, thermal power plans, highways, industrial plants, etc.) near the agricultural area of production. An instrument to assess this potential risk, related to the presence of pollution sources, may be a “Precaution Index”. This index was developed starting from the inventory and geo-referencing of all pollution sources in the study area, using a GIS software. To take into account the relevance of each source, a multi-criteria decision analysis (MCDM), that is ideal to support spatial decision making process, was used. Results are represented in a digital map of “Precaution Index” for the considered regions.