Non-structural carbohydrates (NSC) in plant tissue are frequently quantified to make inferences about plant responses to environmental conditions. Laboratories publishing estimates of NSC of woody plants use many different methods to evaluate NSC. We asked whether NSC estimates in the recent literature could be quantitatively compared among studies. We also asked whether any differences among laboratories were related to the extraction and quantification methods used to determine starch and sugar concentrations. These questions were addressed by sending sub-samples collected from five woody plant tissues, which varied in NSC content and chemical composition, to 29 laboratories. Each laboratory analyzed the samples with their laboratory-specific protocols, based on recent publications, to determine concentrations of soluble sugars, starch and their sum, total NSC. Laboratory estimates differed substantially for all samples. For example, estimates for Eucalyptus globulus leaves (EGL) varied from 23 to 116 (mean = 56) mg g(-1) for soluble sugars, 6-533 (mean = 94) mg g-1 for starch and 53-649 (mean = 153) mg g-1 for total NSC. Mixed model analysis of variance showed that much of the variability among laboratories was unrelated to the categories we used for extraction and quantification methods (method category R-2 = 0.05-0.12 for soluble sugars, 0.10-0.33 for starch and 0.01-0.09 for total NSC). For EGL, the difference between the highest and lowest least squares means for categories in the mixed model analysis was 33 mg g-1 for total NSC, compared with the range of laboratory estimates of 596 mg g-1. Laboratories were reasonably consistent in their ranks of estimates among tissues for starch (r = 0.41-0.91), but less so for total NSC (r = 0.45-0.84) and soluble sugars (r = 0.11-0.83). Our results show that NSC estimates for woody plant tissues cannot be compared among laboratories. The relative changes in NSC between treatments measured within a laboratory may be comparable within and between laboratories, especially for starch. To obtain comparable NSC estimates, we suggest that users can either adopt the reference method given in this publication, or report estimates for a portion of samples using the reference method, and report estimates for a standard reference material. Researchers interested in NSC estimates should work to identify and adopt standard methods.

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Non-structural carbohydrates in woody plants compared among laboratories.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics.

Italian ryegrass (Lolium multiflorum) is an important cool-season, annual forage crop for the grassland rotation system in Southern China. The primary aim of breeding programs is always to seek to improve forage quality in the animal productivity system; however, it is time- and labor-consuming when analyzed excessive large number of samples. The main objectives of this study were to construct near-infrared reflectance spectroscopy (NIRS) models to predict the forage chemistry quality of Italian ryegrass including the concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and water soluble carbohydrate (WSC). The results showed that a broader range of CP, NDF, ADF and WSC contents (%DM) were obtained (4.45-30.60, 21.29-60.47, 11.66-36.17 and 3.95-51.52, respectively) from the samples selected for developing NIRS models. In addition, the critical wavelengths identified in this study to construct optimal NIRS models were located in 4,247-6,102 and 4,247-5,450 cm-1 for CP and NDF content, and both wavelengths 5,446-6,102 and 4,247-4,602 cm-1 could for ADF and WSC. Finally, the optimal models were developed based on the laboratory data and the spectral information by partial least squares (PLS) regression, with relatively high coefficients of determination (R2CV, CP = 0.99, NDF = 0.94, ADF = 0.92, WSC = 0.88), ratio of prediction to devitation (RPD, CP = 8.58, NDF = 4.25, ADF = 3.64, WSC = 3.10). The further statistics of prediction errors relative to laboratory (PRL) and the range error ratio (RER) give excellent assessments of the models with the PRL ratios lower than 2 and the RER values greater than 10. The NIRS models were validated using a completely independent set of samples and have coefficients of determination (R2V, CP = 0.99, NDF = 0.91, ADF = 0.95, WSC = 0.91) and ratio of prediction to deviation (RPD, CP = 9.37, NDF = 3.44, ADF = 4.40, WSC = 3.39). The result suggested that routine screening for forage quality parameters with large numbers of samples is available with the NIRS model in Italian ryegrass breeding programs, as well as facilitating graziers to monitor the forage development stage for improving grazing efficiency.

Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum

Near-infrared reflectance spectroscopy (NIRS) is frequently used for the assessment of key nutrients of forage or crops but remains underused in ecological and physiological studies, especially to quantify non-structural carbohydrates. The aim of this study was to develop calibration models to assess the content in soluble sugars (fructose, glucose, sucrose) and starch in foliar material of Eucalyptus globulus. A partial least squares (PLS) regression was used on the sample spectral data and was compared to the contents measured using standard wet chemistry methods. The calibration models were validated using a completely independent set of samples. We used key indicators such as the ratio of prediction to deviation (RPD) and the range error ratio to give an assessment of the performance of the calibration models. Accurate calibration models were obtained for fructose and sucrose content (R2 > 0.85, root mean square error of prediction (RMSEP) of 0.95%–1.26% in the validation models), followed by sucrose and total soluble sugar content (R2 ~ 0.70 and RMSEP > 2.3%). In comparison to the others, calibration of the starch model performed very poorly with RPD = 1.70. This study establishes the ability of the NIRS calibration model to infer soluble sugar content in foliar samples of E. globulus in a rapid and cost-effective way. We suggest a complete redevelopment of the starch analysis using more specific quantification such as an HPLC-based technique to reach higher performance in the starch model. Overall, NIRS could serve as a high-throughput phenotyping tool to study plant response to stress factors.

Application of near-infrared spectroscopy for estimation of non-structural carbohydrates in foliar samples of Eucalyptus globulus Labilladière.

Flowering and grain-filling stages are highly sensitive to heat and drought stress exposure, leading to significant loss in crop yields. Therefore, phenotyping to enhance resilience to these abiotic stresses is critical for sustaining genetic gains in crop improvement programs. However, traditional methods for screening traits related to these stresses are slow, laborious, and often expensive. Remote sensing provides opportunities to introduce low-cost, less biased, high-throughput phenotyping methods to capture large genetic diversity to facilitate enhancement of stress resilience in crops. This review focuses on four key physiological traits and processes that are critical in understanding crop responses to drought and heat stress during reproductive and grain-filling periods. Specifically, these traits include: (i) time of day of flowering, to escape these stresses during flowering; (ii) optimizing photosynthetic efficiency; (iii) storage and translocation of water-soluble carbohydrates; and (iv) yield and yield components to provide in-season yield estimates. Moreover, we provide an overview of current advances in remote sensing in capturing these traits, and discuss the limitations with existing technology as well as future direction of research to develop high-throughput phenotyping approaches. In the future, phenotyping these complex traits will require sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines.

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Bottlenecks and opportunities in field-based high-throughput phenotyping for heat and drought stress.

The aim of this work was to study the potential of near-infrared reflectance spectroscopy (NIRS) to predict non-structural carbohydrates (NSC), water soluble carbohydrates (WSC), in vitro organic dry matter digestibility (IVOMD), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and starch in samples of whole plant maize with a wide range of variability. The samples were analyzed in reflectance mode by a spectrophotometer FOSS NIRSystems 6500. Four hundred and fifty samples of wide spectrum from different origin were selected out of 3000 scanned for the calibration set, whereas 87 independent random samples were used in the external validation. The goodness of the calibration models was evaluated using the following statistics: coefficient of determination (R2), standard error of cross-validation (SECV), standard error of prediction for external validation (SEP) and the RPDCV and RPDP indexes [ratios of standard deviation (SD) of reference analysis data to SECV and SEP, respectively]. The smaller the SECV and SEP and the greater the RPDCV and RPDP, the predictions are better. Trait measurement units were g/100g of dry matter (DM), except for IVOMD (g/100g OM). The SECV and RPDCV statistics of the calibration set were 1.34 and 3.2 for WSC, 2.57 and 3 for NSC and 2.3 and 2.2 for IVOMD, respectively. The SEP and RPDP statistics for external validation were 0.74 and 4.7 for WSC, 2.14 and 2.5 for NSC and 1.68 and 1.6 for IVOMD respectively. It can be concluded that the NIRS technique can be used to predict WSC and NSC with good accuracy, whereas prediction of IVOMD showed a lesser accuracy. NIRS predictions of OM, CP, NDF, ADF and starch also showed good accuracy.

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NIRS determination of non-structural carbohydrates, water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability

Concern for the preservation of the environment is a constant at present. The European Commi ssion has developed specific directives to prevent pollution and degradation of the environment. In agriculture, organic farming is presented as an alternative to intensive agri culture that is more polluting and straining on natural resources. Most of the maize breeding materials currently commerci alized are not adapted to organic farming conditions and the gene pool has been shrinking aft er years of genetic selection. Therefore, the lan- draces used in breeding programs could introduce new genes to ob tain materials adapted to new agriculture systems. The aim of this work was to evaluate forage maize hybrids d eveloped at Centro de Investigaciones Agrarias de Mabegondo (CIAM) under Organic Farming. These hybrids were obtained by crossing different Galician landraces with an inbred line which has Galician l andrace background. These hybrids have shown good levels of genetic diversity of all studied traits. They w ere more vigorous and earlier than commercial ones. They had variability of fiber and lignin content but simila r digestibility of in vitro organic matter, thus breeding programs could discern and exploit these traits. Therefore, landra ces could provide new genes to current breed- ing programs in order to obtain materials more suited to new agri cultural conditions.

L. Campo

Papers

NIRS determination of non-structural carbohydrates, water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability

Landraces of forage maize as source of genetic variability for organic farming