| Records |
| Author |
Conradt, T.; Gornott, C.; Wechsung, F. |
| Title |
Extending and improving regionalized winter wheat and silage maize yield regression models for Germany: Enhancing the predictive skill by panel definition through cluster analysis |
Type |
Journal Article |
| Year |
2016 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal  |
Agricultural and Forest Meteorology |
| Volume |
216 |
Issue |
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Pages |
68-81 |
| Keywords |
cluster analysis; crop yield estimation; germany; multivariate regression; silage maize; winter wheat; climate-change; canadian prairies; crop yield; temperature; responses; environments; variability; cultivar; china |
| Abstract |
Regional agricultural yield assessments allowing for weather effect quantifications are a valuable basis for deriving scenarios of climate change effects and developing adaptation strategies. Assessing weather effects by statistical methods is a classical approach, but for obtaining robust results many details deserve attention and require individual decisions as is demonstrated in this paper. We evaluated regression models for annual yield changes of winter wheat and silage maize in more than 300 German counties and revised them to increase their predictive power. A major effort of this study was, however, aggregating separately estimated time series models (STSM) into panel data models (PDM) based on cluster analyses. The cluster analyses were based on the per-county estimates of STSM parameters. The original STSM formulations (adopted from a parallel study) contained also the non-meteorological input variables acreage and fertilizer price. The models were revised to use only weather variables as estimation basis. These consisted of time aggregates of radiation, precipitation, temperature, and potential evapotranspiration. Altering the input variables generally increased the predictive power of the models as did their clustering into PDM. For each crop, five alternative clusterings were produced by three different methods, and similarities between their spatial structures seem to confirm the existence of objective clusters about common model parameters. Observed smooth transitions of STSM parameter values in space suggest, however, spatial autocorrelation effects that could also be modeled explicitly. Both clustering and autocorrelation approaches can effectively reduce the noise in parameter estimation through targeted aggregation of input data. (C) 2015 Elsevier B.V. All rights reserved. |
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| Language |
English |
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Series Issue |
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Edition |
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| ISSN |
0168-1923 |
ISBN |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4709 |
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| Author |
Özkan, Ş.; Hill, J.; Cullen, B. |
| Title |
Effect of climate variability on pasture-based dairy feeding systems in south-east Australia |
Type |
Journal Article |
| Year |
2014 |
Publication |
Animal Production Science |
Abbreviated Journal |
Animal Production Science |
| Volume |
55 |
Issue |
9 |
Pages |
1106-1116 |
| Keywords |
carry-forward surplus; conserved-hay; probability; winter deficit; grown forage consumption; new-zealand; nutritive characteristics; interannual variation; botanical composition; herbage accumulation; crop; systems; cows; management; profit |
| Abstract |
The Australian dairy industry relies primarily on pasture for its feed supply. However, the variability in climate affects plant growth, leading to uncertainty in dryland pasture supply. This paper models the impact of climate variability on pasture production and examines the potential of two pasture-based dairy feeding systems: (1) to experience winter deficits; (2) to carry forward the conserved pasture surpluses as silage for future use; and (3) to conserve pasture surpluses as hay. The two dairy feeding systems examined were a traditional perennial ryegrass-based feeding system (ryegrass max. – RM) and a system that incorporated double cropping into the perennial ryegrass pasture base (complementary forage – CF). The conditional probability of the RM and CF systems to generate pasture deficits in winter were 94% and 96%, respectively. Both systems could carry forward the surplus silage into the following lactation almost once in every 4-5 years with the RM system performing slightly better than the CF system. The proportions of the grain-based concentrates fed in the two systems were 25% and 27% for the RM and CF systems, respectively. This study suggests that double-cropping systems have the potential to provide high-quality feed to support the feed gaps when pasture is not available due to increased variability in climatic conditions. |
| Address |
2015-09-23 |
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English |
Summary Language |
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Edition |
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| ISSN |
1836-5787 |
ISBN |
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Article |
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| Notes |
LiveM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4689 |
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| Author |
Bulak, P.; Walkiewicz, A.; Brzezińska, M. |
| Title |
Plant growth regulators-assisted phytoextraction |
Type |
Journal Article |
| Year |
2014 |
Publication |
Biologia Plantarum |
Abbreviated Journal |
Biol. Plant. |
| Volume |
58 |
Issue |
1 |
Pages |
1-8 |
| Keywords |
auxins; cytokinins; gibberelins; heavy metals; phytoremediation; pollutants; Zea-mays l.; heavy-metals; Pteris-vittata; organic-acids; molecular-mechanisms; contaminated soils; Sedum-alfredii; lead uptake; hyperaccumulation; phytoremediation |
| Abstract |
Plant growth regulators (PRG)-assisted phytoremediation is a technique that could enhance the yield of heavy metal accumulation in plant tissues. So far, a small number of experiments have helped identify three groups of plant hormones that may be useful for this purpose: auxins, cytokinins, and gibberellins. Studies have shown that these hormones positively affect the degree of accumulation of metallic impurities and improve the growth and stress resistance of plants. This review summarizes the present knowledge about PGRs’ impact on phytoextraction yield. |
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English |
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Edition |
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0006-3134 |
ISBN |
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Medium |
Review |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4515 |
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| Author |
Semenov, M.A.; Pilkington-Bennett, S.; Calanca, P. |
| Title |
Validation of ELPIS 1980-2010 baseline scenarios using the observed European Climate Assessment data set |
Type |
Journal Article |
| Year |
2013 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
| Volume |
57 |
Issue |
1 |
Pages |
1-9 |
| Keywords |
climate change; impact assessment; downscaling; lars-wg; stochastic weather generators; diverse canadian climates; lars-wg; aafc-wg; radiation; impacts |
| Abstract |
Local-scale daily climate scenarios are required for assessment of climate change impacts. ELPIS is a repository of local-scale climate scenarios for Europe, which are based on the LARS-WG weather generator and future projections from 2 multi-model ensembles, CMIP3 and EU-ENSEMBLES. In ELPIS, the site parameters for the 1980-2010 baseline scenarios were estimated by LARS-WG using daily weather from the European Crop Growth Monitoring System (CGMS) used in many European agricultural assessment studies. The objective of this paper was to compare ELPIS baseline scenarios with observed daily weather obtained independently from the European Climate Assessment (ECA) data set. Several statistical tests were used to compare distributions of climatic variables derived from ECA-observed daily weather and ELPIS-generated baseline scenarios. About 30% of selected sites have a difference in altitude of > 50 m compared with the CGMS grid-cell altitude that was selected to represent agricultural land within a grid-cell. Differences in altitude can explain significant Kolmogorov-Smirnov test (KS-test) results for distribution of daily temperature and in t-tests for temperature monthly means, because of the well-known negative correlation between temperature and elevation. For daily precipitation, the KS-test showed little difference between generated and observed data; however, the more sensitive t-test showed significant results for the sites where altitude differences were large. Approximately 11% of sites showed small positive or negative bias in monthly solar radiation, although 86% sites showed > 3 significant t-test results for monthly means. These results can be explained by differences in conversion of sunshine hours to solar radiation used in CGMS and LARS-WG. We conclude that, considering the limitations above, ELPIS baseline scenarios are suitable for agricultural impact assessments in Europe. |
| Address |
2016-10-31 |
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English |
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Edition |
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0936-577x 1616-1572 |
ISBN |
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Article |
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Conference |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4812 |
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| Author |
Mansouri, M.; Dumont, B.; Destain, M.-F. |
| Title |
Modeling and prediction of nonlinear environmental system using Bayesian methods |
Type |
Journal Article |
| Year |
2013 |
Publication |
Computers and Electronics in Agriculture |
Abbreviated Journal |
Computers and Electronics in Agriculture |
| Volume |
92 |
Issue |
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Pages |
16-31 |
| Keywords |
state and parameter estimation; variational filter; particle filter; extended kalman filter; nonlinear environmental system; leaf area index and soil moisture model; extended kalman filter; state-space models; parameter-estimation; particle filters; navigation; tutorial; tracking |
| Abstract |
An environmental dynamic system is usually modeled as a nonlinear system described by a set of nonlinear ODEs. A central challenge in computational modeling of environmental systems is the determination of the model parameters. In these cases, estimating these variables or parameters from other easily obtained measurements can be extremely useful. This work addresses the problem of monitoring and modeling a leaf area index and soil moisture model (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), particle filter (PF), and the more recently developed technique variational filter (VF). Specifically, two comparative studies are performed. In the first comparative study, the state variables (the leaf-area index LAI, the volumetric water content of the soil layer 1, HUR1 and the volumetric water content of the soil layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of number of estimated model parameters on the accuracy and convergence of these techniques are also assessed. The results of both comparative studies show that the PF provides a higher accuracy than the EKF, which is due to the limited ability of the EKF to handle highly nonlinear processes. The results also show that the VF provides a significant improvement over the PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the VF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The results of the second comparative study show that, for all techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. However, the VF can still provide both convergence as well as accuracy related advantages over other estimation methods. (C) 2013 Elsevier B.V. All rights reserved. |
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English |
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Edition |
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| ISSN |
0168-1699 |
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Article |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4495 |
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