Ruiu, L. M., Maurizi, S., Sassu, S., Seddaiu, G., Zuin, O., Blackmore, C., et al. (2017). Re-Staging La Rasgioni: lessons learned from transforming a traditional form of conflict resolution to engage stakeholders in agricultural water governance. Water, 9(4), 297.
Abstract: This paper presents an informal process inspired by a public practice of conflict mediation used until a few decades ago in Gallura (NE Sardinia, Italy), named La Rasgioni (The Reason). The aim is twofold: (i) to introduce an innovative method that translates the complexity of water-related conflicts into a “dialogical tool”, aimed at enhancing social learning by adopting theatrical techniques; and (ii) to report the outcomes that emerged from the application of this method in Arborea, the main dairy cattle district and the only nitrate-vulnerable zone in Sardinia, to mediate contrasting positions between local entrepreneurs and representatives of the relevant institutions. We discuss our results in the light of four pillars, adopted as research lenses in the International research Project CADWAGO (Climate Change Adaptation and Water Governance), which consider the specific “social–ecological” components of the Arborea system, climate change adaptability in water governance institutions and organizations, systemic governance (relational) practices, and governance learning. The combination of the four CADWAGO pillars and La Rasgioni created an innovative dialogical space that enabled stakeholders and researchers to collectively identify barriers and opportunities for effective governance practices. Potential wider implications and applications of La Rasgioni process are also discussed in the paper.
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Semenov, M. A., Mitchell, R. A. C., Whitmore, A. P., Hawkesford, M. J., Parry, M. A. J., & Shewry, P. R. (2012). Shortcomings in wheat yield predictions. Nat. Clim. Change, 2(6), 380–382.
Abstract: Predictions of a 40–140% increase in wheat yield by 2050, reported in the UK Climate Change Risk Assessment, are based on a simplistic approach that ignores key factors affecting yields and hence are seriously misleading.
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Asseng, S., Ewert, F., Martre, P., Rötter, R. P., Lobell, D. B., Cammarano, D., et al. (2014). Rising temperatures reduce global wheat production. Nat. Clim. Change, 5(2), 143–147.
Abstract: Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time.
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Seddaiu, G., Iocola, I., Farina, R., Orsini, R., Iezzi, G., & Roggero, P. P. (2016). Long term effects of tillage practices and N fertilization in rainfed Mediterranean cropping systems: durum wheat, sunflower and maize grain yield. European Journal of Agronomy, 77, 166–178.
Abstract: Long term investigations on the combined effects of tillage systems and other agronomic practices such as mineral N fertilization under Mediterranean conditions on durum wheat are very scanty and findings are often contradictory. Moreover, no studies are available on the long term effect of the adoption of conservation tillage on grain yield of maize and sunflower grown in rotation with durum wheat under rainfed Mediterranean conditions. This paper reports the results of a 20-years experiment on a durum wheat-sunflower (7 years) and durum wheat–maize (13 years) two-year rotation, whose main objective was to quantify the long term effects of different tillage practices (CT = conventional tillage; MT = minimum tillage; NT = no tillage) combined with different nitrogen fertilizer rates (N0, N1, N2 corresponding to 0, 45 and 90 kg N ha−1 for sunflower, and 0, 90 and 180 kg N ha−1 for wheat and maize) on grain yield, yield components and yield stability for the three crops. In addition, the influence of meteorological factors on the interannual variability of studied variables was also assessed. For durum wheat, NT did not allow substantial yield benefits leading to comparable yields with respect to CT in ten out of twenty years. For both sunflower and maize, NT under rainfed conditions was not a viable options, because of the unsuitable (i.e., too wet) soil conditions of the clayish soil at sowing. Both spring crops performed well with MT. No significant N × tillage interaction was found for the three crops. As expected, the response of durum wheat and maize grain yield to N was remarkable, while sunflower grain yield was not significantly influenced by N rate. Wheat yield was constrained by high temperatures in January during tillering and drought in April during heading. The interannual yield variability of sunflower was mainly associated to soil water deficit at flowering and air temperature during seed filling. Heavy rains during this latter phase strongly constrained sunflower grain yield. Maize grain yield was negatively affected by high temperatures in June and drought in July, this latter factor was particularly important in the fertilized maize. Considering both yield and yield stability, durum wheat and sunflower performed better under MT and N1 while maize performed better under both CT and MT and with N2 rates. The results of this long term study are suitable for supporting policies on sustainable Mediterranean rainfed cropping systems and also for cropping system modelling.
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