TY  - JOUR
AU  - Kostić, Marko
AU  - Ljubičić, Nataša
AU  - Aćin, Vladimir
AU  - Mirosavljević, Milan
AU  - Buđen, Maša
AU  - Rajković, Miloš
AU  - Dedović, Nebojša
PY  - 2024
UR  - http://fiver.ifvcns.rs/handle/123456789/4551
AB  - The ambition of this study was to justify the possibility of wheat trait prediction using a normalized difference vegetation index (NDVI) from a newly developed Plant-O-Meter sensor. Acquired data from Plant-O-Meter was matched with GreenSeeker’s, which was designated as a reference. The experiment was carried out in the field during the 2022 growing season at the long-term experimental field. The experimental design included five different winter wheat genotypes and 20 different NPK fertilizer treatments. The GreenSeeker sensor always gave out NDVI values that were higher than those of the Plant-O-Meter by, on average, 0.029 (6.36%). The Plant-O-Meter sensor recorded similar NDVI values (94% of the variation is explained, P<0.01). The Plant-O-Meter’s NDVIs had a higher CV for different wheat varieties and different sensing dates. For almost all varieties, GreenSeeker exceeded Plant-O-Meter in predicting yields for the early (March 21st) and late (June 6th) growing seasons. NDVIGreenSeeker data improved yield modeling performance by an average of 5.1% when compared to NDVIPlant-O-Meter; in terms of plant height prediction, NDVIGreenSeeker was 3% more accurate than NDVIPlant-O-Meter and no changes in spike length prediction were found. A compact, economical and user-friendly solution, the Plant-O-Meter, is straightforward to use in wheat breeding programs as well as mercantile wheat production.
PB  - Roma : Italian Society of Agricultural Engineering
T2  - Journal of Agricultural Engineering
T1  - An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics
IS  - 1
SP  - 1559
VL  - 55
DO  - 10.4081/jae.2024.1559
ER  - 

@article{
author = "Kostić, Marko and Ljubičić, Nataša and Aćin, Vladimir and Mirosavljević, Milan and Buđen, Maša and Rajković, Miloš and Dedović, Nebojša",
year = "2024",
abstract = "The ambition of this study was to justify the possibility of wheat trait prediction using a normalized difference vegetation index (NDVI) from a newly developed Plant-O-Meter sensor. Acquired data from Plant-O-Meter was matched with GreenSeeker’s, which was designated as a reference. The experiment was carried out in the field during the 2022 growing season at the long-term experimental field. The experimental design included five different winter wheat genotypes and 20 different NPK fertilizer treatments. The GreenSeeker sensor always gave out NDVI values that were higher than those of the Plant-O-Meter by, on average, 0.029 (6.36%). The Plant-O-Meter sensor recorded similar NDVI values (94% of the variation is explained, P<0.01). The Plant-O-Meter’s NDVIs had a higher CV for different wheat varieties and different sensing dates. For almost all varieties, GreenSeeker exceeded Plant-O-Meter in predicting yields for the early (March 21st) and late (June 6th) growing seasons. NDVIGreenSeeker data improved yield modeling performance by an average of 5.1% when compared to NDVIPlant-O-Meter; in terms of plant height prediction, NDVIGreenSeeker was 3% more accurate than NDVIPlant-O-Meter and no changes in spike length prediction were found. A compact, economical and user-friendly solution, the Plant-O-Meter, is straightforward to use in wheat breeding programs as well as mercantile wheat production.",
publisher = "Roma : Italian Society of Agricultural Engineering",
journal = "Journal of Agricultural Engineering",
title = "An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics",
number = "1",
pages = "1559",
volume = "55",
doi = "10.4081/jae.2024.1559"
}

Kostić, M., Ljubičić, N., Aćin, V., Mirosavljević, M., Buđen, M., Rajković, M.,& Dedović, N.. (2024). An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics. in Journal of Agricultural Engineering
Roma : Italian Society of Agricultural Engineering., 55(1), 1559.
https://doi.org/10.4081/jae.2024.1559

Kostić M, Ljubičić N, Aćin V, Mirosavljević M, Buđen M, Rajković M, Dedović N. An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics. in Journal of Agricultural Engineering. 2024;55(1):1559.
doi:10.4081/jae.2024.1559 .

Kostić, Marko, Ljubičić, Nataša, Aćin, Vladimir, Mirosavljević, Milan, Buđen, Maša, Rajković, Miloš, Dedović, Nebojša, "An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics" in Journal of Agricultural Engineering, 55, no. 1 (2024):1559,
https://doi.org/10.4081/jae.2024.1559 . .

TY  - JOUR
AU  - Ljubičić, Nataša
AU  - Popović, Vera
AU  - Kostić, Marko
AU  - Pajić, Miloš
AU  - Buđen, Maša
AU  - Gligorević, Kosta
AU  - Dražić, Milan
AU  - Bižić, Milica
AU  - Crnojević, Vladimir
PY  - 2023
UR  - http://fiver.ifvcns.rs/handle/123456789/3577
AB  - Evaluating maize genotypes under different conditions is important for identifying which genotypes combine stability with high yield potential. The aim of this study was to assess stability and the effect of the genotype–environment interaction (GEI) on the grain yield traits of four maize genotypes grown in field trials; one control trial without nitrogen, and three applying different levels of nitrogen (0, 70, 140, and 210 kg ha−1, respectively). Across two growing seasons, both the phenotypic variability and GEI for yield traits over four maize genotypes (P0725, P9889, P9757 and P9074) grown in four different fertilization treatments were studied. The additive main effects and multiplicative interaction (AMMI) models were used to estimate the GEI. The results revealed that genotype and environmental effects, such as the GEI effect, significantly influenced yield, as well as revealing that maize genotypes responded differently to different conditions and fertilization measures. An analysis of the GEI using the IPCA (interaction principal components) analysis method showed the statistical significance of the first source of variation, IPCA1. As the main component, IPCA1 explained 74.6% of GEI variation in maize yield. Genotype G3, with a mean grain yield of 10.6 t ha−1, was found to be the most stable and adaptable to all environments in both seasons, while genotype G1 was found to be unstable, following its specific adaptation to the environments.
PB  - Basel : MDPI
T2  - Plants - Basel
T1  - Multivariate Interaction Analysis of Zea mays L. Genotypes Growth Productivity in Different Environmental Conditions
IS  - 11
SP  - 2156
VL  - 12
DO  - 10.3390/plants12112165
ER  - 

@article{
author = "Ljubičić, Nataša and Popović, Vera and Kostić, Marko and Pajić, Miloš and Buđen, Maša and Gligorević, Kosta and Dražić, Milan and Bižić, Milica and Crnojević, Vladimir",
year = "2023",
abstract = "Evaluating maize genotypes under different conditions is important for identifying which genotypes combine stability with high yield potential. The aim of this study was to assess stability and the effect of the genotype–environment interaction (GEI) on the grain yield traits of four maize genotypes grown in field trials; one control trial without nitrogen, and three applying different levels of nitrogen (0, 70, 140, and 210 kg ha−1, respectively). Across two growing seasons, both the phenotypic variability and GEI for yield traits over four maize genotypes (P0725, P9889, P9757 and P9074) grown in four different fertilization treatments were studied. The additive main effects and multiplicative interaction (AMMI) models were used to estimate the GEI. The results revealed that genotype and environmental effects, such as the GEI effect, significantly influenced yield, as well as revealing that maize genotypes responded differently to different conditions and fertilization measures. An analysis of the GEI using the IPCA (interaction principal components) analysis method showed the statistical significance of the first source of variation, IPCA1. As the main component, IPCA1 explained 74.6% of GEI variation in maize yield. Genotype G3, with a mean grain yield of 10.6 t ha−1, was found to be the most stable and adaptable to all environments in both seasons, while genotype G1 was found to be unstable, following its specific adaptation to the environments.",
publisher = "Basel : MDPI",
journal = "Plants - Basel",
title = "Multivariate Interaction Analysis of Zea mays L. Genotypes Growth Productivity in Different Environmental Conditions",
number = "11",
pages = "2156",
volume = "12",
doi = "10.3390/plants12112165"
}

Ljubičić, N., Popović, V., Kostić, M., Pajić, M., Buđen, M., Gligorević, K., Dražić, M., Bižić, M.,& Crnojević, V.. (2023). Multivariate Interaction Analysis of Zea mays L. Genotypes Growth Productivity in Different Environmental Conditions. in Plants - Basel
Basel : MDPI., 12(11), 2156.
https://doi.org/10.3390/plants12112165

Ljubičić N, Popović V, Kostić M, Pajić M, Buđen M, Gligorević K, Dražić M, Bižić M, Crnojević V. Multivariate Interaction Analysis of Zea mays L. Genotypes Growth Productivity in Different Environmental Conditions. in Plants - Basel. 2023;12(11):2156.
doi:10.3390/plants12112165 .

Ljubičić, Nataša, Popović, Vera, Kostić, Marko, Pajić, Miloš, Buđen, Maša, Gligorević, Kosta, Dražić, Milan, Bižić, Milica, Crnojević, Vladimir, "Multivariate Interaction Analysis of Zea mays L. Genotypes Growth Productivity in Different Environmental Conditions" in Plants - Basel, 12, no. 11 (2023):2156,
https://doi.org/10.3390/plants12112165 . .