• Authors:
    • Watson,James
    • Challinor,Andrew J.
    • Fricker,Thomas E.
    • Ferro,Christopher A. T.
  • Source: Climatic Change
  • Volume: 132
  • Issue: 1
  • Year: 2015
  • Summary: Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.
  • Authors:
    • Lee,Hyunok
    • Sumner,Daniel A.
  • Source: Climatic Change
  • Volume: 132
  • Issue: 4
  • Year: 2015
  • Summary: This article establishes quantitative relationships between the evolution of climate and cropland using daily climate data for a century and data on allocation of land across crops for six decades in a specific agro-climatic region of California. These relationships are applied to project how climate scenarios reported by the Intergovernmental Panel on Climate Change would drive cropland patterns into 2050. Projections of warmer winters, particularly from 2035 to 2050, cause lower wheat area and more alfalfa and tomato area. Only marginal changes in area were projected for tree and vine crops, in part because although lower, chill hours remain above critical values.
  • Authors:
    • Visser,F.
    • Dargusch,P.
    • Smith,C.
    • Grace,P. R.
  • Source: Journal of Cleaner Production
  • Volume: 103
  • Year: 2015
  • Summary: The various initiatives in the market place to quantify the sustainability levels of products are putting pressure on farmers to demonstrate a reduction in the environmental impacts of their crop management practices, and in particular with the lowering of the carbon footprints of their crops. At present there is no internationally accredited common method or carbon footprint model which generates site specific and LCA aligned emission estimates. The application of the Crop Carbon Progress Calculator (CCAP) is demonstrated for an irrigated cotton 'farm to ship' case study in Australia where we determine that the progress made in the 2011 crop against a 2002 crop base year amounts to 44% reduction in GHG emission levels. We estimate that for this particular case study the total carbon footprint of producing a bale of cotton up to ship's side or point of export is 323 kg CO 2e. This includes 182 kg CO 2e from the farm production phase, 73.1 kg CO 2e from the gin to port supply chain, and 68.1 kg CO 2e that results from emission from the stock piled gin trash at the gins. It appears that a feasible option to avoid these trash emissions is to incorporate the waste at farm level. Our analysis shows that this could generate an emissions credit of 48.8 kg CO 2e per bale at farm level, which will amount to a 27% reduction in the farm emissions footprint and a 15% reduction in the whole farm to ship carbon footprint. Due to a number of site specific environmental and crop management factors, there can be significant variances in crop carbon footprint outcomes.
  • Authors:
    • Hu, W.
    • Cao, Y.
    • Xu, J.
    • Wang, Y.
    • Peng, Z.
    • Wang, H.
    • Han, X.
    • Xiong, W.
    • Lin, E.
    • Ju, H.
    • Huang, H.
    • Li, Y.
  • Source: Agriculture, Ecosystems & Environment
  • Volume: 209
  • Year: 2015
  • Summary: Drought is one of the major climatic disasters intimidating winter wheat production in the Huang-Huai-Hai (3H) Plain of China. The yield damage caused by drought tends to increase in the future, indicated by a pronounced uprising of drought events under RCP 8.5 scenario in terms of its affecting magnitude and area. This paper presents a modeling approach by using crop model DSSAT and hydrological indices to assess the vulnerability of winter wheat to future potential drought, based on an integrated assessment of exposure, sensitivity and adaptive capacity. Our results demonstrate that Beijing, Tianjin, Hebei and Shandong are more exposed and sensitive to potential drought than other regions in 3H. Traditional irrigation has the greater benefits in northern 3H Plain than southern regions, but is still insufficient to impede the yield loss due to potential drought. Under RCP 8.5 emission scenario and the period of 2010-2050, the worst drought effect is projected to occur around 2030. More than half of 3H plain are subject to high drought vulnerability. With increasing drought risks, we suggest immediate and appropriate adaptation actions to be taken before 2030s, especially in Shandong and Hebei, the most vulnerable provinces of 3H plain.
  • Authors:
    • Siebert, S.
    • Wolf, J.
    • Hoffmann, H.
    • Webber, H.
    • Gang, Z.
    • Ewert, F.
  • Source: Primary Research Article
  • Volume: 21
  • Issue: 11
  • Year: 2015
  • Summary: This study evaluates the impacts of projected climate change on irrigation requirements and yields of six crops (winter wheat, winter barley, rapeseed, grain maize, potato, and sugar beet) in Europe. Furthermore, the uncertainty deriving from consideration of irrigation, CO 2 effects on crop growth and transpiration, and different climate change scenarios in climate change impact assessments is quantified. Net irrigation requirement (NIR) and yields of the six crops were simulated for a baseline (1982-2006) and three SRES scenarios (B1, B2 and A1 B, 2040-2064) under rainfed and irrigated conditions, using a process-based crop model, SIMPLACE . We found that projected climate change decreased NIR of the three winter crops in northern Europe (up to 81 mm), but increased NIR of all the six crops in the Mediterranean regions (up to 182 mm yr -1). Climate change increased yields of the three winter crops and sugar beet in middle and northern regions (up to 36%), but decreased their yields in Mediterranean countries (up to 81%). Consideration of CO 2 effects can alter the direction of change in NIR for irrigated crops in the south and of yields for C3 crops in central and northern Europe. Constraining the model to rainfed conditions for spring crops led to a negative bias in simulating climate change impacts on yields (up to 44%), which was proportional to the irrigation ratio of the simulation unit. Impacts on NIR and yields were generally consistent across the three SRES scenarios for the majority of regions in Europe. We conclude that due to the magnitude of irrigation and CO 2 effects, they should both be considered in the simulation of climate change impacts on crop production and water availability, particularly for crops and regions with a high proportion of irrigated crop area.
  • Authors:
    • Peridy, N.
    • Zouabi, O.
  • Source: Article
  • Volume: 133
  • Issue: 2
  • Year: 2015
  • Summary: North African countries (NACs) are particularly concerned with climate change because of their geographical position (close to deserts) and their economic dependence on agriculture. We aim to provide additional insight into the impact of climate on agriculture for NACs, through the example of Tunisia. We first use disaggregated data, both at the geographical level (for 24 regions in Tunisia) and at the product level (cereals, olives, citrus fruit, tomatoes, potatoes and palm trees). Second, through spatial panel data analysis, we explore both the time and spatial dimensions of the data. This makes it possible to consider spatial interactions in agricultural production and the role of climate in these spatial spillover effects. Finally, the model not only includes direct climate variables, such as temperature and precipitation, but also indirect climate-related variables such as the stock of water in dams and groundwater. Results show that Tunisian agriculture is strongly dependent on the direct effects of temperature and precipitation for all the products considered at the regional level. The presence of dams and groundwater generally has a positive effect on agricultural production for irrigated crops with interesting spillover effects with neighboring regions. However, this impact is still considerably lessened in the case of detrimental climate conditions (indirect effect). These results raise the question of the sustainability of the growth in agricultural production in Tunisia in the case of significant climate change.
  • Authors:
    • Vincenzi, F.
    • Racchetti, E.
    • Soana, E.
    • Castaldelli, G.
    • Fano, E.
    • Bartoli, M.
  • Source: Agriculture, Ecosystems & Environment
  • Volume: 212
  • Year: 2015
  • Summary: Within irrigated agricultural watersheds, canal networks may play a crucial role as nitrogen (N) sink. This is due to the intertwined action of macrophytes and microbial communities occurring in the dense net of small watercourses. We hypothesize that vegetated canals may buffer relevant fractions of excess N from agriculture via microbial denitrification, and that vegetation provides multiple interfaces that greatly support the activity of bacteria. To test these hypotheses, we measured net dinitrogen (N 2) fluxes in bare sediments and at the reach-scale in vegetated ditches. As study areas we selected canals subjected to diffuse N pollution, laying in a lowland sub-basin of the Po River (northern Italy). Denitrification was evaluated on the basis of changes in dissolved N 2:Ar, measured by Membrane Inlet Mass Spectrometry. Complementary data were obtained via upstream-downstream inorganic N balances and intact core incubations targeting sedimentary N fluxes. Denitrification was the major pathway for N removal, with rates at the reach-scale (5-25 mmol N m -2 d -1) up to one order of magnitude higher than in sediment alone (3-7 mmol N m -2 d -1). Results highlighted that N uptake by macrophyte stands was quantitatively small; however, aquatic vegetation provided multiple interfaces for microbial growth and N-related processes. Our data suggest that 1 ha of vegetated canal may remove between 150 and 560 kg N yr -1. In the study area, an average canal density of ~0.05 linear km ha -1 of agricultural land has the potential to buffer 5-17% of the excess N from agriculture (~60 kg N ha -1 yr -1). The results of this study suggest the central role of emergent vegetation in promoting microbial N-transformation and canal self-depuration. Innovative management of the canal networks should couple hydraulic needs with the maintenance of emergent vegetation.
  • Authors:
    • Merino-de-Miguel, S.
    • Sanchez-Giron, V.
    • Litago, J.
    • Inclan, R.
    • Schmid, T.
    • Uribe, C.
    • Huesca, M.
    • Rodriguez-Rastrero, M.
    • Cicuendez, V.
    • Palacios-Orueta, A.
  • Source: Agriculture, Ecosystems & Environment
  • Volume: 212
  • Year: 2015
  • Summary: The assessment of soil respiration processes in agroecosystems is essential to understand the C balance and to study the effects of soil respiration on climate change. The use of spectral data through remote sensing techniques constitutes a valuable tool to study ecological processes such as the C cycle dynamics. The objective of this work was to evaluate the potential to assess total (Rs) and autotrophic (Ra) soil respiration through spectral information acquired by field spectroscopy in a row irrigated corn crop ( Zea mays L.) throughout the growing period. The relationships between Rs and Ra with leaf area index (LAI), spectral indexes and abiotic factors (soil moisture and soil temperature) were assessed by linear regression models using the adjusted coefficient of determination (Radj 2) to measure and compare the proportion of variance explained by the models. Results showed significant differences and a high variability in the relationships between Rs and Ra with spectral indexes within the corn field during the phenological stages and in measurements under the plants and between the rows. Best results were obtained when assessing Ra during vegetative stages. However, during the reproductive stages, spectral indexes were better related to Rs which could be related to the presence of rhizomicrobial respiration linked to vegetation activity. Spectral indexes contain significant functional information, beyond mere structural changes, that could be related to carbon fluxes. However, specific models should be applied for the different phenological stages and there is a need to be cautious when upscaling remote sensing models. The results obtained confirm that in irrigated crop systems remote sensing data can produce relevant information to assess both Rs and Ra through spectral indexes.
  • Authors:
    • Kucukalbay, M.
    • Akbolat, D.
  • Source: POLISH JOURNAL OF ENVIRONMENTAL STUDIES
  • Volume: 23
  • Issue: 4
  • Year: 2014
  • Summary: This study determined carbon dioxide (CO2) emissions from the cultivation of chickpeas cultivated in Usak using conventional wheat-chickpea crop rotation methods as a function of conventional tillage (CT), reduced tillage (RT), and direct seeding (DS). Measurements of carbon dioxide (CO2) emissions from the soil were started after planting using a portable CO2 measurement system (PP System) for a period of 55 days. Our results indicated CO2 emissions at rates of 4.1, 4.5, and 5.3 g.m(-2).h(-1) in response to the CT, RT, and DS treatments, respectively. A significant difference was found between CT and RT, and CO2 emissions under the DS treatment were higher than those of the other two treatments (p<0.05). Soil evaporation rates were estimated at 11.6, 10.9, and 13.1 g.m(-2).h(-1) under the CT, RT, and DS treatments, respectively. Mean soil temperafure was 17.5, 18.1, and 18.3 degrees C for the CT, RT, and DS treatments, respectively (p<0.05). Mean values of soil moisture content (wet base) after tillage were 19.7%, 19.1%, and 18.8% for CT, RT, and DS, respectively. Soil temperature and seedbed preparation methods appeared to influence soil CO2 emissions.
  • Authors:
    • Smucker, A. J. M.
    • Basso, B.
    • Zhang, W.
    • Kavdir, Y.
  • Source: JOURNAL OF SOIL AND WATER CONSERVATION
  • Volume: 69
  • Issue: 5
  • Year: 2014