Person: Araus, J.L.
Loading...
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Araus
First Name
J.L.
Name
Araus, J.L.
ORCID ID
0000-0002-8866-2388 19 results
Search Results
Now showing 1 - 10 of 19
- Regional monitoring of Fall Armyworm (FAW) using early warning systems(MDPI, 2022) Buchaillot, M.L.; Cairns, J.E.; Hamdziripi, E.; Wilson, K.; Hughes, D.; Chelal, J.; McCloskey, P.; Kehs, A.; Clinton, N.; Araus, J.L.; Kefauver, S.C.
Publication - Relative contribution of shoot and ear photosynthesis to grain filling in wheat under good agronomical conditions assessed by differential organ Delta13C(Oxford University Press, 2014) Sanchez-Bragado, R.; Molero, G.; Reynolds, M.P.; Araus, J.L.
Publication - NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions(Akadémiai Kiadó, 2011) Cabrera-Bosquet, L.; Molero, G.; Stellacci, A.M.; Bort, J.; Nogues, S.; Araus, J.L.
Publication - Leaf versus whole-canopy remote sensing methodologies for crop monitoring under conservation agriculture: a case of study with maize in Zimbabwe(Nature Publishing Group, 2020) Gracia-Romero, A.; Kefauver, S.C.; Vergara Diaz, O.; Hamdziripi, E.; Zaman-Allah, M.; Thierfelder, C.; Prasanna, B.M.; Cairns, J.E.; Araus, J.L.
Publication - Transgenic solutions to increase yield and stability in wheat: shining hope or flash in the pan?(Oxford University Press, 2019) Araus, J.L.; Serret, M.D.; Lopes, M.Second-generation transgenic crops have the potential to transform agriculture, but progress has been limited, and particularly so in wheat where no transgenic cultivar has yet been approved. Taking on the challenge, González et al. (2019) report that transgenic wheat lines carrying a mutated version of the sunflower transcription factor (HaHB4), belonging to the homeodomain-leucine zipper family (HD-Zip I), had increased yield and water use efficiency across a range of environments, with particular benefits under stress. It is an important step forward in an area where progress is urgently needed, though it is too early to claim that transgenic wheat will form the backbone of a second Green Revolution.
Publication - Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques(MDPI, 2019) Buchaillot, M.L.; Gracia-Romero, A.; Vergara Diaz, O.; Zaman-Allah, M.; Tarekegne, A.T.; Cairns, J.E.; Prasanna, B.M.; Araus, J.L.; Kefauver, S.C.Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.
Publication - Translating high-throughput phenotyping into genetic gain(Elsevier, 2018) Araus, J.L.; Kefauver, S.C.; Zaman-Allah, M.; Olsen, M.; Cairns, J.E.Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and ‘phenotypers’ in an effective manner.
Publication - Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe(MDPI, 2018) Gracia-Romero, A.; Vergara Diaz, O.; Thierfelder, C.; Cairns, J.E.; Kefauver, S.C.; Araus, J.L.In the coming decades, Sub-Saharan Africa (SSA) faces challenges to sustainably increase food production while keeping pace with continued population growth. Conservation agriculture (CA) has been proposed to enhance soil health and productivity to respond to this situation. Maize is the main staple food in SSA. To increase maize yields, the selection of suitable genotypes and management practices for CA conditions has been explored using remote sensing tools. They may play a fundamental role towards overcoming the traditional limitations of data collection and processing in large scale phenotyping studies. We present the result of a study in which Red-Green-Blue (RGB) and multispectral indexes were evaluated for assessing maize performance under conventional ploughing (CP) and CA practices. Eight hybrids under different planting densities and tillage practices were tested. The measurements were conducted on seedlings at ground level (0.8 m) and from an unmanned aerial vehicle (UAV) platform (30 m), causing a platform proximity effect on the images resolution that did not have any negative impact on the performance of the indexes. Most of the calculated indexes (Green Area (GA) and Normalized Difference Vegetation Index (NDVI)) were significantly affected by tillage conditions increasing their values from CP to CA. Indexes derived from the RGB-images related to canopy greenness performed better at assessing yield differences, potentially due to the greater resolution of the RGB compared with the multispectral data, although this performance was more precise for CP than CA. The correlations of the multispectral indexes with yield were improved by applying a soil-mask derived from a NDVI threshold with the aim of corresponding pixels with vegetation. The results of this study highlight the applicability of remote sensing approaches based on RGB images to the assessment of crop performance and hybrid choice.
Publication - Comparative performance of ground vs. aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization(Frontiers, 2017) Gracia-Romero, A.; Kefauver, S.C.; Vergara Diaz, O.; Zaman-Allah, M.; Prasanna, B.M.; Cairns, J.E.; Araus, J.L.Low soil fertility is one of the factors most limiting agricultural production, with phosphorus deficiency being among the main factors, particularly in developing countries. To deal with such environmental constraints, remote sensing measurements can be used to rapidly assess crop performance and to phenotype a large number of plots in a rapid and cost-effective way. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and multispectral (visible and infrared) data as phenotypic traits and crop monitoring tools for early assessment of maize performance under phosphorus fertilization. Thus, a set of 26 maize hybrids grown under field conditions in Zimbabwe was assayed under contrasting phosphorus fertilization conditions. Remote sensing measurements were conducted in seedlings at two different levels: at the ground and from an aerial platform. Within a particular phosphorus level, some of the RGB indices strongly correlated with grain yield. In general, RGB indices assessed at both ground and aerial levels correlated in a comparable way with grain yield except for indices a* and u*, which correlated better when assessed at the aerial level than at ground level and Greener Area (GGA) which had the opposite correlation. The Normalized Difference Vegetation Index (NDVI) evaluated at ground level with an active sensor also correlated better with grain yield than the NDVI derived from the multispectral camera mounted in the aerial platform. Other multispectral indices like the Soil Adjusted Vegetation Index (SAVI) performed very similarly to NDVI assessed at the aerial level but overall, they correlated in a weaker manner with grain yield than the best RGB indices. This study clearly illustrates the advantage of RGB-derived indices over the more costly and time-consuming multispectral indices. Moreover, the indices best correlated with GY were in general those best correlated with leaf phosphorous content. However, these correlations were clearly weaker than against grain yield and only under low phosphorous conditions. This work reinforces the effectiveness of canopy remote sensing for plant phenotyping and crop management of maize under different phosphorus nutrient conditions and suggests that the RGB indices are the best option.
Publication - Photosynthetic contribution of the ear to grain filling in wheat: a comparison of different methodologies for evaluation(Oxford University Press, 2016) Sanchez-Bragado, R.; Molero, G.; Reynolds, M.P.; Araus, J.L.The culm (particularly the flag leaf) and the ear are believed to play a major role in providing assimilates for grain filling in wheat. However, the results obtained in the past varied depending on the methodology applied. Three different methodologies were compared that aimed to assess the relative contribution of the culm (photosynthetic organs below the ear) and the ear to grain filling. The first two consisted of applications of photosynthesis inhibition treatments, including the use of the herbicide DCMU and organ shading. The third was a non-intrusive method that compared the carbon isotope composition (δ13C) of mature kernels with the δ13C of the water-soluble fraction of the peduncle, awns and glumes. Several advanced CIMMYT lines were tested under good agronomic conditions. The δ13C approach assigned a higher photosynthetic contribution to the ear than to the culm. However, some methodological considerations should be taken into account when applying the δ13C approach, particularly the sampling method used, in order to prevent post-harvest respiration. The shading approach assigned a similar contribution to the ear as to the culm. The DCMU approach assigned a greater role to the culm but herbicide application to the culm affected the ear, thus biasing the final grain weight. Moreover DCMU and shading approaches may cause compensatory effects which overestimated the contribution of unaffected organs. This study may help to develop precise phenotyping tools to identify physiological traits such as ear photosynthesis that could contribute towards increasing grain yield.
Publication