Identifying Factors Explaining Yield Variability of Miscanthus x giganteus and Miscanthus sinensis Across Contrasting Environments: Use of an Agronomic Diagnosis Approach

Miscanthus is a perennial C4 crop whose lignocellulose can be used as an alternative to the production of biosourced material. Miscanthus x giganteus (M. x giganteus) has demonstrated high maximum yields but also high yield variability across farmers’ fields. Miscanthus sinensis (M. sinensis) can be an alternative to M. x giganteus because it is considered to be more tolerant to water stress and to produce more stable yields. This study aimed to identify the main factors explaining the variability of yields across site-years for M. x giganteus and M. sinensis. A multi-local and multi-year trial network was set up in France (Ile de France and Center regions). Four treatments were established on seven sites, from spring 2013 to winter 2019: at each site, two treatments of M. x giganteus (a treatment from rhizome and a treatment from rhizome-derived plantlets) and two treatments of M. sinensis (a treatment from seed-derived plantlets established in single density and a treatment from seed-derived plantlets established in double density). We experienced 5 years of harvest because miscanthus was not harvested in 2014. First, we characterized yield variations across site-years for both genotypes. Second, we defined and calculated a set of indicators (e.g., water stress indicator, sum of degree-days of the previous year, number of frost days) that could affect miscanthus yields. Finally, we performed a mixed model with re-sampling to identify the main indicators that explained yield variability for each genotype specifically. Results showed that water stress and crop age mainly explained yield variability for both genotypes. M. sinensis yields were also affected by the sum of degree-days of the previous year of growth. Hence, genotype choice must take into account environmental characteristics. M. sinensis could indeed achieve higher and more stable yields than those of M. x giganteus in shallow sandy soils or in locations with a higher risk of low rainfall.

Sunflower agronomy: 10 years of research in partnership within the “Sunflower” Technological Joint Unit (UMT) in Toulouse

In order to make more efficient plant breeding and gain in competitiveness, the sector of oil-protein crops decided to intensify agronomic research on sunflower crop. The “Sunflower” Joint Technological Unit (Unité Mixte Technologique (UMT) “Tournesol”, in French) was launched in the Toulouse area in 2006, associating closely INRA and Terres Inovia. First focused on improving oil production through an agronomic approach, the UMT was renewed in 2011 with a broader partnership and a more assertive orientation towards the development of decision-making tools. The objective of this paper is to highlight the relevance and productivity of this user-oriented research facility. The main results relate to (i) the co-construction of a simulation model (SUNFLO) that can be parameterized and manipulated by Terres Inovia engineers, (ii) the joint exploration of supra-field scales and new methods for agronomic diagnosis and yield forecasting based on remote sensing, (iii) the tuning and dissemination of operational decision rules, (iv) the production of essential knowledge on emergent and/or damaging fungal diseases, as well as on complex interactions between genotype, environment and crop management. After a concluding symposium in 2016, new requests for sunflower research were formulated by the participants. They also advocated for a diversification of crops to consider in order to better meet the needs of the whole oil-protein sector.

Fluorescence and Reflectance Spectroscopy for Early Detection of Different Mycorrhized Plantain Plants

<p class="1Body">Sustainable agriculture with use of Arbuscular Mycorrhizal Fungi (AMF) is an emerging farm management that improves crops nutrient and water use efficiency. Decision making on the effect of AMF is still dependent on agronomic diagnosis which is long, tedious, expensive and destructive. This study demonstrates the applicability of proximal fluorescence and reflectance spectroscopy for evaluating and detecting at early stage distinct types of mycorrhized plantain from two cultivars (<em>Musa paradisiaca</em>).</p><p class="1Body">Visible-near infrared (400-1000 nm) reflectance and fluorescence data were collected from control and three levels mycorrhized plants designed in randomized and complete block under greenhouse conditions. Two spectral measurements at a week interval were performed on plant leaves by using an USB spectrometer mounted with an Arduino-based LED driver clip.</p>A new normalized reflectance water NWI5 index shows with Datt5 alone highly significant differences at P&lt;0.001 respectively for Orishele and fhia21 cultivars. dNIRmin920_980, NDVI3 and GI reflectance index are significant at P&lt;0.01. Seven other reflectance and 3 fluorescence indices ANTH, FRF_R and NBI_R are significant at P&lt;0.05. The two first principal components for each cultivar spectral features explaining 94.1 % of variance were used to build predictive classification models. LogitBoost algorithm indicates accuracy of 90.27% on stratified cross-validation and 87.5% on test split. Our results confirm that fluorescence and reflectance spectroscopy is a valuable tool for early assessment of mycorrhization success rate evaluation and pattern recognition. They also show promise for the development of non-destructive and cost-effective detectors in monitoring crops under biofertilizers with arbuscular mycorrhizae.