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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.

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.

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.

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.