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Rusu, T.; Moraru, P.; Coste, C.; Cacovean, H.; Chetan, F.; Chetan, C. |
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Title  |
Impact of climate change on climatic indicators in Transylvanian Plain, Romania |
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Journal Article |
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Year |
2014 |
Publication |
Journal of Food, Agriculture and Environment |
Abbreviated Journal |
Journal of Food, Agriculture and Environment |
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Volume |
12 |
Issue |
1 |
Pages |
469-473 |
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Keywords |
Climate change; climatic indicators; Transylvanian plain |
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Abstract |
The condition of land degradation in Transylvanian Plain and its effects, being the result of local extreme physical-geographical conditions, is susceptible to degradation (evidenced by the erodibility index), which overlaps the extreme climatic conditions. Thermal and hydric regime monitoring is necessary in order to identify and implement measures of adaptation to the impacts of climate change. Soil moisture and temperature regimes were evaluated using a set of 20 data logging stations positioned throughout the plain. Each station stores electronic data of ground temperature at 3 depths (10, 30, 50 cm), the humidity at the depth of 10 cm, the air temperature (at 1 m) and precipitations. Climate change in the past few years has significantly altered the climatic indicators of the Transylvanian Plain. Precipitations, although deficient in terms of annual amounts, through their regime, have a negative influence on the plant carpet. Pluvial aggressiveness index reveals, for the research period, a first peak of pluvial aggressiveness during the months of February-April, then in July and in autumn, the months of October-November. This requires special measures for soil conservation, both in autumn and early spring, soil tillage measures being recommended, which ensure the presence of plant debris and vegetation in early spring but especially in summer and autumn. Climatic indicators determined for the period 2008 – 2012 point out, in Transylvanian Plain, a semi-arid Mediterranean climate through the rain factor Lang, respectively semi-arid (in the South) – semi-wet (in the North) according to the De Martonne index. This climatic characterization requires special technological measures for soil conservation. |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4638 |
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Lehtonen, H.; Palosuo, T.; Korhonen, P.; Liu, X. |
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Title |
Higher Crop Yield Levels in the North Savo Region—Means and Challenges Indicated by Farmers and Their Close Stakeholders |
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Journal Article |
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Year |
2018 |
Publication |
Agriculture |
Abbreviated Journal |
Agriculture |
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8 |
Issue |
7 |
Pages |
93 |
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northern Europe; forage grasslands; spring cereals; drainage; soil conidtions; farm management; agricultural policy |
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The sustainable intensification of farming systems is expected to increase food supply and reduce the negative environmental effects of agriculture. It is also seen as an effective adaptation and mitigation strategy in response to climate change. Our aim is to determine farmers’ and other stakeholders’ views on how higher crop yields can be achieved from their currently low levels. This was investigated in two stakeholder workshops arranged in North Savo, Finland, in 2014 and 2016. The workshop participants, who were organized in discussion groups, considered some agricultural policies to discourage the improvement of crop yields. Policy schemes were seen to support extensification and reduce the motivation for yield improvements. However, the most important means for higher crop yields indicated by workshop participants were improved soil conditions with drainage and liming, in addition to improved crop rotations, better sowing techniques, careful selection of cultivars and forage grass mixtures. Suggested solutions for improving both crop yields and farm income also included optimized use of inputs, focusing production at the most productive fields and actively developed farming skills and knowledge sharing. These latter aspects were more pronounced in 2016, suggesting that farmers’ skills are increasingly being perceived as important. |
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2077-0472 |
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TradeM, ft_macsur |
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MA @ admin @ |
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5203 |
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Sanz-Cobena, A.; Sánchez-Martín, L.; García-Torres, L.; Vallejo, A. |
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Gaseous emissions of N2O and NO and NO3 − leaching from urea applied with urease and nitrification inhibitors to a maize (Zea mays) crop |
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Journal Article |
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2012 |
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Agriculture, Ecosystems and Environment |
Abbreviated Journal |
Agric. Ecosyst. Environ. |
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149 |
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64-73 |
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Urease inhibitor; Nitrogen losses; Irrigation; Nitrification |
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Urea has become the predominant source of synthetic nitrogen (N) fertilizer used throughout the world. Among the various available mitigation tools, urease inhibitors like NBPT have the most potential to improve efficiency of urea by reducing N losses, mainly via ammonia volatilization. However, there is a lack of information on the effect of N-(n-butyl) thiophosphoric triamide (NBPT) on other N losses such as gaseous emissions of N2O and NO and NO3− leaching. A two-year field experiment using irrigated maize (Zea mays) crop was carried out under Mediterranean conditions to evaluate the effectiveness of urea coated with NBPT (0.4%, w/w) alone and with both NBPT and nitrification inhibitor dicyandiamide (DCD) (0.4 and 3%, w/w, respectively) to mitigate N2O–N, NO–N and NO3−–N losses. The different treatments of U, U+NBPT and U+NBPT+DCD were applied to the maize crop in 2009 and then in 2010. The 2010 maize crop followed a fallow period, during which the 2009 crop residues were incorporated into the soil. Two different irrigation regimes were followed each year. In 2009, irrigation was controlled for the first 2 weeks following urea fertilization; whereas, the 2010 crop period was characterized by increased irrigation in the same period. After each treatment application, measurements of the changes in soil mineral N, gaseous emissions of N2O and NO, nitrate leaching and biomass production were made. N2O emissions were effectively abated by NBPT and NBPT+DCD and were reduced by 54 and 24%, respectively, in 2009. A reduction in nitrification rate by the inhibitors was also observed during 2009. In 2010 cropping period, NBPT reduced N2O emissions by 4%; while the combination of NBPT and DCD treatment reduced N2O emission by 43%. Yield-scaled N2O emissions were reduced by 50 and 18% by NBPT and the mixture of NBPT+DCD, respectively, in 2009. Applying inhibitors did not have any significant effect on yield-scaled N2O emissions in the 2010 crop period. Total NO losses from urea were 2.25 kg NO–N ha−1 in the 2009 crop period and 5 times lower in the following year; this may provide an indicator of the prevalence of nitrification as the main process in the production of N2O in the 2009 maize crop. Most of the NO3− was lost within the fallow period (i.e. 92, 81 and 75% of the total NO3− leached for U, U+NBPT and U+NBPT+DCD, respectively), so the incorporation of crop residues was not as effective as expected at reducing these N losses. Our study suggests that the effectiveness of NBPT and combination of NBPT+DCD in reducing N losses from applied urea is influenced by management practices, such as irrigation, and climatic conditions. |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4593 |
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Mansouri, M.; Dumont, B.; Leemans, V.; Destain, M.-F. |
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Title |
Bayesian methods for predicting LAI and soil water content |
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Journal Article |
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Year |
2014 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
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15 |
Issue |
2 |
Pages |
184-201 |
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Keywords |
crop model; bayes; data assimilation; extended kalman filtering; particle filtering; variational filtering; leaf-area index; parameter-estimation; crop models; moisture; instruments; management; sensors; state |
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LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: extended Kalman filtering (EKF), particle filtering (PF), and variational filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation. |
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1385-2256 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4629 |
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Dumont, B.; Leemans, V.; Ferrandis, S.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Assessing the potential of an algorithm based on mean climatic data to predict wheat yield |
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Journal Article |
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Year |
2014 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
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15 |
Issue |
3 |
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255-272 |
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Keywords |
stics model; yield prediction; real-time; proxy-sensing; stochastic weather generator; crop yield; mediterranean environment; simulation-model; variability; nitrogen; ensembles; forecasts; demeter; europe |
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The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days. |
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1385-2256 1573-1618 |
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CropM |
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MA @ admin @ |
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4621 |
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