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Zhao, G., Hoffmann, H., Yeluripati, J., Xenia, S., Nendel, C., Coucheney, E., et al. (2016). Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops. Env. Model. Softw., 80, 100–112.
Abstract: We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known.
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Yin, X., Olesen, J. E., Wang, M., Öztürk, I., Zhang, H., & Chen, F. (2016). Impacts and adaptation of the cropping systems to climate change in the Northeast Farming Region of China. European Journal of Agronomy, 78, 60–72.
Abstract: The Northeast Farming Region of China (NFR) is a very important crop growing area, comprising seven sub-regions: Xing’anling (XA), Sanjiang (SJ), Northwest Songliao (NSL), Central Songliao (CSL), Southwest Songliao (SSL), Changbaishan (CB) and Liaodong (LD), which has been severely affected by extreme climate events and climatic change. Therefore, a set of expert survey has been done to identify current and project future climate limitations to crop production and explore appropriate adaptation measures in NFR. Droughts have been the largest limitation for maize (Zea mays L.) in NSL and SSL, and for soybean (Glycine max L Merr.) in SSL. Chilling damage has been the largest limitation for rice (Oryza sativa L) production in XA, SJ and CB. Projected climate change is expected to be beneficial for expanding the crop growing season, and to provide more suitable conditions for sowing and harvest. Autumn frost will occur later in most parts of NFR, and chilling damage will also decrease, particularly for rice production in XA and SJ. Drought and heat stress are expected to become more severe for maize and soybean production in most parts of NFR. Also, plant diseases, pests and weeds are considered to become more severe for crop production under climate change. Adaptation measures that have already been implemented in recent decades to cope with current climatic limitations include changes in timing of cultivation, variety choice, soil tillage practices, crop protection, irrigation and use of plastic film for soil cover. With the projected climate change and increasing risk of climatic extremes, additional adaptation measures will become relevant for sustaining and improving productivity of crops in NFR to ensure food security in China. (C) 2016 Elsevier B.V. All rights reserved.
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Webber, H., Ewert, F., Kimball, B. A., Siebert, S., White, J. W., Wall, G. W., et al. (2016). Simulating canopy temperature for modelling heat stress in cereals. Env. Model. Softw., 77, 143–155.
Abstract: Crop models must be improved to account for the effects of heat stress events on crop yields. To date, most approaches in crop models use air temperature to define heat stress intensity as the cumulative sum of thermal times (TT) above a high temperature threshold during a sensitive period for yield formation. However, observational evidence indicates that crop canopy temperature better explains yield reductions associated with high temperature events than air temperature does. This study presents a canopy level energy balance using Monin ObukhovSimilarity Theory (MOST) with simplifications about the canopy resistance that render it suitable for application in crop models and other models of the plant environment. The model is evaluated for a uniform irrigated wheat canopy in Arizona and rainfed maize in Burkina Faso. No single variable regression relationships for key explanatory variables were found that were consistent across sowing dates to explain the deviation of canopy temperature from air temperature. Finally, thermal times determined with simulated canopy temperatures were able to reproduce thermal times calculated with observed canopy temperature, whereas those determined with air temperatures were not. (C) 2015 Elsevier Ltd. All rights reserved.
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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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Moriondo, M., Ferrise, R., Trombi, G., Brilli, L., Dibari, C., & Bindi, M. (2015). Modelling olive trees and grapevines in a changing climate. Env. Model. Softw., 72, 387–401.
Abstract: The models developed for simulating olive tree and grapevine yields were reviewed by focussing on the major limitations of these models for their application in a changing climate. Empirical models, which exploit the statistical relationship between climate and yield, and process based models, where crop behaviour is defined by a range of relationships describing the main plant processes, were considered. The results highlighted that the application of empirical models to future climatic conditions (i.e. future climate scenarios) is unreliable since important statistical approaches and predictors are still lacking. While process-based models have the potential for application in climate-change impact assessments, our analysis demonstrated how the simulation of many processes affected by warmer and CO2-enriched conditions may give rise to important biases. Conversely, some crop model improvements could be applied at this stage since specific sub-models accounting for the effect of elevated temperatures and CO2 concentration were already developed. (C) 2014 Elsevier Ltd. All rights reserved.
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