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Near-real-time, high-spatial-resolution leaf area index (LAI) maps would enable producers to monitor crop health and growth status, improving agricultural practices such as fertiliser and water management. LAI retrieval methods are numerous and can be divided into statistical and physically based methods. While statistical methods are generally subject to high site-specificity but possess high ease of implementation and use, physically based methods are more transferable, albeit more complex to use in operational settings. In addition, statistical methods need a large amount of data for calibration and subsequent validation, and this is only seldom feasible. Techniques based on predictive equations (PEphysical) represent a viable alternative, allowing the partial combination of statistical and physical methods merits while minimising their shortcomings. In this paper, predictive equation-based techniques were compared with four other methods: two radiative transfer model (RTM) inversion methods, one based on neural network (NNET) and one based on a look-up table (LUT), and two empirical methods (one using empirical models based on vegetation indices and in situ data and one based on empirical models found in the scientific literature). The methods were chosen based on common use. To evaluate the performance of the studied methods, the coefficient of determination (R2), root mean square error (RMSE), and normalised root mean square error (nRMSE, %) between the estimates and in situ LAI measurements were reported. The best PEphysical results, achieved by the OSAVI index (RMSE = 0.84 m2 m−2), provided better performance for LAI recovery than the NNET-based RTM inversions (0.86 m2 m−2) or the estimates made by LUT (0.94 m2 m−2). Furthermore, the best PEphysical produced accuracies comparable to the best empirical model (RMSE = 0.71 m2 m−2), calibrated through in situ data, and similar to the best literature model (RMSE = 0.76 m2 m−2). These results indicated that PEphysical can be used to recover LAI with transferability comparable to literature models.

Proso millet (Panicum miliaceum L.) is a cereal well known for its ability to be successfully grown under drought and intense heat conditions, thus sustaining food security in arid regions. Considering that a trend of increasing drought severity is expected in the future in Southern Europe, solutions need to be found to enhance the resilience of agroecosystems to the effects of climate change. From this perspective, proso millet re-introduction could represent an interesting tool in reducing water consumption for grain production and in providing a new resource to farmers. The aim of this study was to characterize proso millet adaptability to drought and low-input field conditions in the Mediterranean environment, especially considering water-related traits, such as water use efficiency. Limited water-demanding crops and yield stability can contribute to the resilience of agroecosystems and their adaptation to climate change. A three-year field crop experiment was conducted in northern Italy to assess proso millet’s performance in terms of productivity and water status in rainfed agriculture conditions. It was compared to a conventional irrigated corn, a typical summer cereal of the area. All years of experimentation were characterized by adverse meteorological trends, in the full manifestation of the uncertainties of climate change. Despite such different conditions from an agro-meteorological point of view, proso millet showed, in non-irrigated conditions, stable yield and water use efficiency (on average 0.30 kg/m2 and 1.83 kg/m3, respectively), and good agronomic performance. Proso millet, therefore, seems to offer interesting traits for reintroduction on the European side of the Mediterranean Basin, representing a resource for farmers. Moreover, the shortness of the proso millet life cycle (on average 108 days) allows it to be used as a catch crop in the event of major crop failure, an event becoming more likely in the climate change scenario. Furthermore, the possibility of producing grain while saving water (and other production inputs), even in very hot and dry years, increases the sustainability of agricultural production and the resilience of agroecosystems.

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.