Non-foliar photosynthesis and nitrogen assimilation influence grain yield in durum wheat regardless of water conditions.

There is a need to generate improved crop varieties adapted to the ongoing changes in the climate. We studied durum wheat canopy and central metabolism of six different photosynthetic organs in two yield-contrasting varieties. The aim was to understand the mechanisms associated with the water stress response and yield performance. Water stress strongly reduced grain yield, plant biomass, and leaf photosynthesis, and down-regulated C/N-metabolism genes and key protein levels, which occurred mainly in leaf blades. By contrast, higher yield was associated with high ear dry weight and lower biomass and ears per area, highlighting the advantage of reduced tillering and the consequent improvement in sink strength, which promoted C/N metabolism at the whole plant level. An improved C metabolism in blades and ear bracts and N assimilation in all photosynthetic organs facilitated C/N remobilization to the grain and promoted yield. Therefore, we propose that further yield gains in Mediterranean conditions could be achieved by considering the source-sink dynamics and the contribution of non-foliar organs, and particularly N assimilation and remobilization during the late growth stages. We highlight the power of linking phenotyping with plant metabolism to identify novel traits at the whole plant level to support breeding programmes.

Quinoa panicles contribute to carbon assimilation and are more tolerant to salt stress than leaves.

Contribution of inflorescences to seed filling have attracted great attention given the resilience of this photosynthetic organ to stressful conditions. However, studies have been almost exclusively focused to small grain cereals. In this study, we aimed to explore these responses in quinoa, as a climate resilient seed crop of elevated economic and nutritious potential. We compared the physiological and metabolic performance of panicles and leaves of two quinoa cultivars growing under contrasting salinity levels. Plant growth, photosynthetic and transpiratory gas exchange and chlorophyll fluorescence were monitored in inflorescences and leaves throughout the experiment. At flowering stage, young and mature leaves and panicles were sampled for key metabolic markers related to carbon, nitrogen and secondary metabolisms. When subjected to salt stress, panicles showed attenuated declines on photosynthesis, water use, pigments, amino acids, and protein levels as compared to leaves. In fact, the assimilation rates, together with a high hexose content evidenced an active photosynthetic role of the panicle under optimal and salt stress conditions. Moreover, we also found significant genotypic variability for physiological and metabolic traits of panicles and leaves, which emphasizes the study of genotype-dependent stress responses at the whole plant level. We conclude that quinoa panicles are less affected by salt stress than leaves, which encourages further research and exploitation of this organ for crop improvement and stress resilience considering the high natural diversity.

Analysis of durum wheat photosynthetic organs during grain filling reveals the ear as a water stress-tolerant organ and the peduncle as the largest pool of primary metabolites.

MAIN CONCLUSION: The pool of carbon- and nitrogen-rich metabolites is quantitatively relevant in non-foliar photosynthetic organs during grain filling, which have a better response to water limitation than flag leaves. The response of durum wheat to contrasting water regimes has been extensively studied at leaf and agronomic level in previous studies, but the water stress effects on source-sink dynamics, particularly non-foliar photosynthetic organs, is more limited. Our study aims to investigate the response of different photosynthetic organs to water stress and to quantify the pool of carbon and nitrogen metabolites available for grain filling. Five durum wheat varieties were grown in field trials in the Spanish region of Castile and León under irrigated and rainfed conditions. Water stress led to a significant decrease in yield, biomass, and carbon and nitrogen assimilation, improved water use efficiency, and modified grain quality traits in the five varieties. The pool of carbon (glucose, glucose-6-phosphate, fructose, sucrose, starch, and malate) and nitrogen (glutamate, amino acids, proteins and chlorophylls) metabolites in leaf blades and sheaths, peduncles, awns, glumes and lemmas were also analysed. The results showed that the metabolism of the blades and peduncles was the most susceptible to water stress, while ear metabolism showed higher stability, particularly at mid-grain filling. Interestingly, the total metabolite content per organ highlighted that a large source of nutrients, which may be directly involved in grain filling, are found outside the blades, with the peduncles being quantitatively the most relevant. We conclude that yield improvements in our Mediterranean agro-ecosystem are highly linked to the success of shoots in producing ears and a higher number of grains, while grain filling is highly dependent on the capacity of non-foliar organs to fix CO2 and N. The ear organs show higher stress resilience than other organs, which deserves our attention in future breeding programmes.

Crop phenotyping in a context of global change: What to measure and how to do it.

High-throughput crop phenotyping, particularly under field conditions, is nowadays perceived as a key factor limiting crop genetic advance. Phenotyping not only facilitates conventional breeding, but it is necessary to fully exploit the capabilities of molecular breeding, and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems. In terms of phenotyping, it is necessary to determined which selection traits are relevant in each situation, and which phenotyping tools/methods are available to assess such traits. Remote sensing methodologies are currently the most popular approaches, even when lab-based analyses are still relevant in many circumstances. On top of that, data processing and automation, together with machine learning/deep learning are contributing to the wide range of applications for phenotyping. This review addresses spectral and red-green-blue sensing as the most popular remote sensing approaches, alongside stable isotope composition as an example of a lab-based tool, and root phenotyping, which represents one of the frontiers for field phenotyping. Further, we consider the two most promising forms of aerial platforms (unmanned aerial vehicle and satellites) and some of the emerging data-processing techniques. The review includes three Boxes that examine specific case studies.

Leaf versus whole-canopy remote sensing methodologies for crop monitoring under conservation agriculture: a case of study with maize in Zimbabwe.

Enhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R < 0.700) and grain yield (GY) (R < 0.800). Canopy indices reported even higher correlation coefficients when assessing GY, especially those based on the measurements of the vegetation density as the green area indices (R < 0.850). Canopy measurements from both ground and aerial platforms performed very similar, but indices assessed from the UAV performed best in capturing the most relevant information from the whole plot and correlations with GY and leaf N content were slightly higher. Leaf-based measurements demonstrated utility in monitoring N leaf content, though canopy measurements outperformed the leaf readings in assessing GY parameters, while providing the additional value derived from the affordability and easiness of using a pheno-pole system or the high-throughput capacities of the UAVs.

Comparison of Selection Traits for Effective Popcorn (Zea mays L. var. Everta) Breeding Under Water Limiting Conditions.

Climate change is expected to intensify water restriction to crops, impacting on the yield potential of crops such as popcorn. This work aimed to evaluate the performance of 10 field cultivated popcorn inbred lines during two growing seasons, under well-watered (WW) and water stressed (WS) (ψsoil≥ -1.5 MPa) conditions. Water stress was applied by withholding irrigation in the phenological phase of male pre-anthesis. Additionally, two contrasting inbred lines, P7 (superior line) and L75 (low performer) were compared for grain yield (GY) and expanded popcorn volume (EPV), selected from previous studies, were tested under greenhouse conditions. In the field, no genotype x water condition x crop season (G×WC×CS) interaction was observed, whereas GY (-51%), EPV (-55%) and leaf greenness (SPAD index) measured 17 days after anthesis (DAA) (> -10%) were highly affected by water limitation. In general, root traits (angles, number, and density) presented G×WC×CS interaction, which did not support their use as selection parameters. In relation to leaf senescence, for both WS and WW conditions, the superior inbred lines maintained a stay-green condition (higher SPAD index) until physiological maturity, but maximum SPAD index values were observed later in WW (48.7 by 14 DAA) than in WS (43.9 by 7 DAA). Under both water conditions, negative associations were observed between SPAD index values 15 and 8 days before anthesis DBA), and GY and EPV (r ≥ -0.69), as well as between SPAD index 7, 17, and 22 DAA, and angles of brace root (AB), number of crown roots (NC) and crown root density (CD), in WS (r ≥ -0.69), and AB and CD, in WW (r ≥ -0.70). Lower NC and CD values may allow further root deepening in WS conditions. Under WS P7 maintained higher net photosynthesis values, stomatal conductance, and transpiration, than L75. Additionally, L75 exhibited a lower (i.e., more negative) carbon isotope composition value than P7 under WS, confirming a lower stomatal aperture in L75. In summary, besides leaf greenness, traits related to leaf photosynthetic status, and stomatal conductance were shown to be good indicators of the agronomic performance of popcorn under water constraint.

Application of Trichoderma asperellum T34 on maize (Zea mays) seeds protects against drought stress.

MAIN CONCLUSION: Coating maize seeds with the microbial plant protection product Trichoderma asperellum strain T34 is an effective form of inoculation that enhances plant performance when faced with drought stress, and it improves nutrient and kernel parameters differently in drought and non-stressed conditions. Drought is currently one of the biggest threats to maize production. Trichoderma spp. is mainly used in agriculture as plant protection product with secondary beneficial effects on plants: improved growth, nutrient uptake and plant immunity. Here, we studied the physiological performance of maize plants under two different water regimes (fully irrigated and drought conditions) and three different seed treatments: application of Trichoderma asperellum strain T34, application of a chemical fungicide (CELEST XL) or the combination of both. Regardless of water regime, T34 treatment improved kernel P and C, kernel number and dry weight. Higher populations of T34 on the rhizosphere (T34 treatment) alleviated water stress better than lower T34 populations (T34+Q treatment). Under drought, T34 treatment improved leaf relative water content, water use efficiency, PSII maximum efficiency and photosynthesis. T34-treated maize seeds maintained sufficient T34 populations to alleviate drought throughout crop development suggesting an optimal dose of 104 and 105 colony forming units g-1 dry weight of rhizosphere under the studied conditions. This work helps to demonstrate the beneficial interaction between T. asperellum strain T34 and maize plants under drought.

Metabolome Profiling Supports the Key Role of the Spike in Wheat Yield Performance.

Although the relevance of spike bracts in stress acclimation and contribution to wheat yield was recently revealed, the metabolome of this organ and its response to water stress is still unknown. The metabolite profiles of flag leaves, glumes and lemmas were characterized under contrasting field water regimes in five durum wheat cultivars. Water conditions during growth were characterized through spectral vegetation indices, canopy temperature and isotope composition. Spike bracts exhibited better coordination of carbon and nitrogen metabolisms than the flag leaves in terms of photorespiration, nitrogen assimilation and respiration paths. This coordination facilitated an accumulation of organic and amino acids in spike bracts, especially under water stress. The metabolomic response to water stress also involved an accumulation of antioxidant and drought tolerance related sugars, particularly in the spikes. Furthermore, certain cell wall, respiratory and protective metabolites were associated with genotypic outperformance and yield stability. In addition, grain yield was strongly predicted by leaf and spike bracts metabolomes independently. This study supports the role of the spike as a key organ during wheat grain filling, particularly under stress conditions and provides relevant information to explore new ways to improve wheat productivity including potential biomarkers for yield prediction.

Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data.

Hyperspectral techniques are currently used to retrieve information concerning plant biophysical traits, predominantly targeting pigments, water, and nitrogen-protein contents, structural elements, and the leaf area index. Even so, hyperspectral data could be more extensively exploited to overcome the breeding challenges being faced under global climate change by advancing high-throughput field phenotyping. In this study, we explore the potential of field spectroscopy to predict the metabolite profiles in flag leaves and ear bracts in durum wheat. The full-range reflectance spectra (visible (VIS)-near-infrared (NIR)-short wave infrared (SWIR)) of flag leaves, ears and canopies were recorded in a collection of contrasting genotypes grown in four environments under different water regimes. GC-MS metabolite profiles were analyzed in the flag leaves, ear bracts, glumes, and lemmas. The results from regression models exceeded 50% of the explained variation (adj-R2 in the validation sets) for at least 15 metabolites in each plant organ, whereas their errors were considerably low. The best regressions were obtained for malate (82%), glycerate and serine (63%) in leaves; myo-inositol (81%) in lemmas; glycolate (80%) in glumes; sucrose in leaves and glumes (68%); γ-aminobutyric acid (GABA) in leaves and glumes (61% and 71%, respectively); proline and glucose in lemmas (74% and 71%, respectively) and glumes (72% and 69%, respectively). The selection of wavebands in the models and the performance of the models based on canopy and VIS organ spectra and yield prediction are discussed. We feel that this technique will likely to be of interest due to its broad applicability in ecophysiology research, plant breeding programmes, and the agri-food industry.

Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques.

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.

Identification of traits associated with barley yield performance using contrasting nitrogen fertilizations and genotypes.

Much attention has been paid to understanding the traits associated with crop performance and the associated underlying physiological mechanisms, with less effort done towards combining different plant scales, levels of observation, or including hybrids of autogamous species. We aim to identify mechanisms at canopy, leaf and transcript levels contributing to crop performance under contrasting nitrogen supplies in three barley genotypes, two hybrids and one commercial line. High nitrogen fertilization did not affect photosynthetic capacity on a leaf area basis and lowered nitrogen partial factor productivity past a certain point, but increased leaf area and biomass accumulation, parameters that were closely tracked using various different high throughput remote sensing based phenotyping techniques. These aspects, together with a larger catabolism of leaf nitrogen compounds amenable to sink translocation, contributed to higher crop production. Better crop yield and growth in hybrids compared to the line was linked to a nitrogen-saving strategy in source leaves to the detriment of larger sink size, as indicated by the lower leaf nitrogen content and downregulation of nitrogen metabolism and aquaporin genes. While these changes did not reduce photosynthesis capacity on an area basis, they were related with better nitrogen use in the hybrids compared with the line.

Leaf dorsoventrality as a paramount factor determining spectral performance in field-grown wheat under contrasting water regimes.

The effects of leaf dorsoventrality and its interaction with environmentally induced changes in the leaf spectral response are still poorly understood, particularly for isobilateral leaves. We investigated the spectral performance of 24 genotypes of field-grown durum wheat at two locations under both rainfed and irrigated conditions. Flag leaf reflectance spectra in the VIS-NIR-SWIR (visible-near-infrared-short-wave infrared) regions were recorded in the adaxial and abaxial leaf sides and at the canopy level, while traits providing information on water status and grain yield were evaluated. Moreover, leaf anatomical parameters were measured in a subset of five genotypes. The spectral traits studied were more affected by the leaf side than by the water regime. Leaf dorsoventral differences suggested higher accessory pigment content in the abaxial leaf side, while water regime differences were related to increased chlorophyll, nitrogen, and water contents in the leaves in the irrigated treatment. These variations were associated with anatomical changes. Additionally, leaf dorsoventral differences were less in the rainfed treatment, suggesting the existence of leaf-side-specific responses at the anatomical and biochemical level. Finally, the accuracy in yield prediction was enhanced when abaxial leaf spectra were employed. We concluded that the importance of dorsoventrality in spectral traits is paramount, even in isobilateral leaves.

Phenotyping Conservation Agriculture Management Effects on Ground and Aerial Remote Sensing Assessments of Maize Hybrids Performance in Zimbabwe.

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.

The Plant-Transpiration Response to Vapor Pressure Deficit (VPD) in Durum Wheat Is Associated With Differential Yield Performance and Specific Expression of Genes Involved in Primary Metabolism and Water Transport.

The regulation of plant transpiration was proposed as a key factor affecting transpiration efficiency and agronomical adaptation of wheat to water-limited Mediterranean environments. However, to date no studies have related this trait to crop performance in the field. In this study, the transpiration response to increasing vapor pressure deficit (VPD) of modern Spanish semi-dwarf durum wheat lines was evaluated under controlled conditions at vegetative stage, and the agronomical performance of the same set of lines was assessed at grain filling as well as grain yield at maturity, in Mediterranean environments ranging from water stressed to good agronomical conditions. A group of linear-transpiration response (LTR) lines exhibited better performance in grain yield and biomass compared to segmented-transpiration response (STR) lines, particularly in the wetter environments, whereas the reverse occurred only in the most stressed trial. LTR lines generally exhibited better water status (stomatal conductance) and larger green biomass (vegetation indices) during the reproductive stage than STR lines. In both groups, the responses to growing conditions were associated with the expression levels of dehydration-responsive transcription factors (DREB) leading to different performances of primary metabolism-related enzymes. Thus, the response of LTR lines under fair to good conditions was associated with higher transcription levels of genes involved in nitrogen (GS1 and GOGAT) and carbon (RCBL) metabolism, as well as water transport (TIP1.1). In conclusion, modern durum wheat lines differed in their response to water loss, the linear transpiration seemed to favor uptake and transport of water and nutrients, and photosynthetic metabolism led to higher grain yield except for very harsh drought conditions. The transpiration response to VPD may be a trait to further explore when selecting adaptation to specific water conditions.

Comparative Performance of Ground vs. Aerially Assessed RGB and Multispectral Indices for Early-Growth Evaluation of Maize Performance under Phosphorus Fertilization.

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.

Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley.

With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively.

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization.

Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R (2)~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization.