Leaf dorsoventrality as a paramount factor determining spectral performance in field-grown wheat under contrasting water regimes
Journal of Experimental Botany, Volume 69, Issue 12, 25 May 2018, Pages 3081–3094, https://doi.org/10.1093/jxb/ery109
New Publication by Fadia Chairi!
Great Work Fadia Chairi on a great publication: Post-Green Revolution genetic advance in durum wheat: the case of Spain, available here for the next 50 days!
https://authors.elsevier.com/a/1XjfU1M2tV9cvv
Field Crops Research
Volume 228, 1 November 2018, Pages 158-169
Post-green revolution genetic advance in durum wheat: The case of Spain
Highlights
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Genetic gain of durum wheat in Spain slowed after the green revolution until reach a plateau in the last decade.
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However, genetic gain was positively related with the mean and maximum daily temperatures of the testing sites.
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The genetic advance was related to improvement in kernels m−2 and kernels spike−1.
New publication in Remote Sensing
Remote Sens. 2018, 10(2), 349; doi:10.3390/rs10020349
Phenotyping Conservation Agriculture Management Effects on Ground and Aerial Remote Sensing Assessments of Maize Hybrids Performance in Zimbabwe
1Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
2International Maize and Wheat Improvement Center, CIMMYT Southern Africa Regional Office, P.O. Box MP163, Harare, Zimbabwe
(This article belongs to the Special Issue Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery)
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. View Full-Text
New Frontiers In Plant Science Article
Great Job Adrian on the first article of your PhD in the #1 Plant Science Journal -https://www.frontiersin.org/articles/10.3389/fpls.2017.02004/abstract
Comparative performance of ground versus aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization
Adrian Gracia-Romero1, 
Shawn C. Kefauver1, 
Omar Vergara-Diaz1, 
Mainassara A. ZAMAN-ALLAH2,Boddupalli M. Prasanna2, 
Jill E. Cairns2 and 
José Luis Araus1*- 1Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Spain
- 2CIMMYT Southern Africa Regional Office, International Maize and Wheat Improvement Center, Zimbabwe
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.
Keywords: Maize, remote sensing, UAV, RGB indices, Multispectral indices, Phosphorous fertilization
Received: 06 Sep 2017; Accepted: 10 Nov 2017.
First blog post
So let’s start up with our new blog site!
In this first post we are going to share with you the wonderful experience of participating for the TV program named “Agrosfera” which appeared on screen on the 27th of June of 2015, in the spanish national television (RTVE).
The staff of the unit starts to appear at minute 7:30 and throughout the video.
Below we provide the link to an embed version of the video, which can be found on the RTVE website:
http://www.rtve.es/drmn/embed/video/3188959
