Patil, R. H., Laegdsmand, M., Olesen, J. E., & Porter, J. R. (2014). Soil temperature manipulation to study global warming effects in arable land: performance of buried heating-cable method. Environment and Ecology Research, 1(4), 196–204.
Abstract: Buried heating-cable method for manipulating soil temperature was designed and tested its performance in large concrete lysimeters grown with the wheat crop in Denmark. Soil temperature in heated plots was elevated by 5℃ compared with that in control by burying heating-cable at 0.1 m depth in a plough layer. Temperature sensors were placed at 0.05, 0.1 and 0.25 m depths in soil, and 0.1 m above the soil surface in all plots, which were connected to an automated data logger. Soil-warming setup was able to maintain a mean seasonal temperature difference of 5.0 ± 0.005℃ between heated and control plots at 0.1 m depth while the mean seasonal rise in soil temperature in the top 0.25 m depth (plough layer) was 3℃. Soil temperature in control plots froze (≤ 0℃) for 15 and 13 days respectively at 0.05 and 0.1 m depths while it did not in heated plots during the coldest period (Nov-Apr). This study clearly showed the efficacy of buried heating-cable technique in simulating soil temperature, and thus offers a simple, effective and alternative technique to study soil biogeochemical processes under warmer climates. This technique, however, decouples below-ground soil responses from that of above-ground vegetation response as this method heats only the soil. Therefore, using infrared heaters seems to represent natural climate warming (both air and soil) much more closely and may be used for future climate manipulation field studies.
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Pasqui, M., & Di Giuseppe, E. (2019). Climate change, future warming, and adaptation in Europe. Animal Frontiers, 9(1), 6–11.
Abstract: In recent decades, the increased temperatures reported in Europe and in the Mediterranean basin represent one of the clearest footprints of climate change along with increased frequency of heat waves. These climate modifications put the environment and human activities under strong pressure with a resulting need for designing new adaptation and mitigation strategies. The climate change challenge is unprecedented for humanity and is recognized as a priority topic for future research. Changes in the way we think and behave are critical challenges at the global and regional levels.
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Palosuo, T., Rotter, R. P., Salo, T., Peltonen-Sainio, P., Tao, F., & Lehtonen, H. (2015). Effects of climate and historical adaptation measures on barley yield trends in Finland. Clim. Res., 65, 221–236.
Abstract: In this study, the WOFOST crop simulation model was used together with comprehensive empirical databases on barley Hordeum vulgare L. to study the contributions of different yield-determining and -limiting factors to observed trends of barley yield in Finland from 1988 to 2008. Simulations were performed at 3 study sites representing different agro-ecological zones, and compared with the data from experimental sites and that reported by local farmers. Yield gaps between simulated potential yields and farmers’ yields and their trends were assessed. Positive observed yield trends of Finnish barley mostly resulted from the development and usage of new, high-yielding cultivars. Simulated trends in climatic potential and water-limited potential yields of individual cultivars showed a slight declining trend. Yield gaps showed an increasing trend in 2 out of 3 study areas. Since the mid-1990s, a major reason for this has been the lack of market and policy incentives favouring crop management decisions, i.e. annual fertilisation, soil maintenance, drainage and crop rotation decisions, aiming for higher yields. The study indicates potential options for increasing or maintaining barley yields in the future. The breeding of new climate-resilient cultivars is the primary option. However, this needs to work alongside overall adjustments to farm management and must be supported by financial incentives for farmers to increase yields.
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Murat, M., Malinowska, I., Gos, M., & Krzyszczak, J. (2018). Forecasting daily meteorological time series using ARIMA and regression models. Int. Agrophys., 32(2), 253–264.
Abstract: The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt-Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.
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Müller, C., & Robertson, R. D. (2014). Projecting future crop productivity for global economic modeling. Agric. Econ., 45(1), 37–50.
Abstract: Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10-38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change.
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