scholarly journals Functional–Structural Plant Modeling Highlights How Diversity in Leaf Dimensions and Tillering Capability Could Promote the Efficiency of Wheat Cultivar Mixtures

2021 ◽  
Vol 12 ◽  
Author(s):  
Emmanuelle Blanc ◽  
Pierre Barbillon ◽  
Christian Fournier ◽  
Christophe Lecarpentier ◽  
Christophe Pradal ◽  
...  

Increasing the cultivated diversity has been identified as a major leverage for the agroecological transition as it can help improve the resilience of low input cropping systems. For wheat, which is the most cultivated crop worldwide in terms of harvested area, the use of cultivar mixtures is spreading in several countries, but studies have seldom focused on establishing mixing rules based on plant architecture. Yet, the aerial architecture of plants and the overall canopy structure are critical for field performance as they greatly influence light interception, plant interactions and yield. The very high number of trait combinations in wheat mixtures makes it difficult to conduct experimentations on this issue, which is why a modeling approach appears to be an appropriate solution. In this study, we used WALTer, a functional structural plant model (FSPM), to simulate wheat cultivar mixtures and try to better understand how differences between cultivars in key traits of the aerial architecture influence mixture performance. We simulated balanced binary mixtures of cultivars differing for different critical plant traits: final height, leaf dimensions, leaf insertion angle and tillering capability. Our study highlights the impact of the leaf dimensions and the tillering capability on the performance of the simulated mixtures, which suggests that traits impacting the plants' leaf area index (LAI) have more influence on the performance of the stand than traits impacting the arrangement of the leaves. Our results show that the performance of mixtures is very variable depending on the values of the explored architectural traits. In particular, the best performances were achieved by mixing cultivars with different leaf dimensions and different tillering capability, which is in agreement with numerous studies linking the diversity of functional traits in plant communities to their productivity. However, some of the worst performances were also achieved by mixing varieties differing in their aerial architecture, which suggests that diversity is not a sufficient criterion to design efficient mixtures. Overall, these results highlight the importance of simulation-based explorations for establishing assembly rules to design efficient mixtures.

2019 ◽  
Vol 11 (17) ◽  
pp. 2002
Author(s):  
Leizhen Liu ◽  
Wenhui Zhao ◽  
Jianjun Wu ◽  
Shasha Liu ◽  
Yanguo Teng ◽  
...  

Solar-induced chlorophyll fluorescence (SIF) is considered to be a potential indicator of photosynthesis. However, the impact of growth and environmental parameters on SIF at different time-scales remains unclear, which has greatly restricted the application of SIF in detecting photosynthesis variations. Thus, in this study, the impact of growth and environmental parameters on SIF was thoroughly clarified. Here, continuous time series of canopy SIF (760 nm, F760) over wheat and maize was measured based on an automated spectroscopy system. Meanwhile, field measurements of growth and environmental parameters were also collected using commercial-grade devices. Relationships of these parameters with F760, apparent SIF (F760/solar radiance, AF760), and SIF yield (F760/canopy radiance of 685 nm, Fy760) were analyzed using principal component analysis (PCA) and Pearson correlation to reveal their impacts on SIF. Results showed that F760 at seasonal and diurnal scales were mainly driven by solar radiation (SWR), leaf area index (LAI), chlorophyll content (Chl), mean leaf inclination angle (MTA), and relative water content (RWC). Other environmental parameters, including air temperature (Ta), relative humidity (Rh), vapor pressure deficit (VPD), and soil moisture (SM), contribute less to the variation of seasonal or diurnal F760. AF760 and Fy760 are likely to be less dependent on Ta, Rh, and VPD due to the removal of the impact from SWR, but an enhanced relationship of AF760 (and Fy760) with SM was observed, particularly under water stress. Compared with F760, wheat AF760 was better correlated to LAI and RWC as expected, while maize AF760 did not show an enhanced relationship with all growth parameters, probably due to its complicated canopy structure. The relationship of wheat Fy760 with canopy structure parameters was further reduced, except for maize measurements. Furthermore, SM-induced water stress and phenological stages should be taken into consideration when we interpret the seasonal and diurnal patterns of SIF since they were closely related to photosynthesis and plant growth (e.g., LAI in our study). To our knowledge, this is the first exploration of the impacts of growth and environmental parameters on SIF based on continuous ground measurements, not only at a seasonal scale but also at a diurnal scale. Our results could provide deep insight into the variation of SIF signals and also promote the further application of SIF in the health assessments of terrestrial ecosystems.


2016 ◽  
Vol 17 (12) ◽  
pp. 3029-3043 ◽  
Author(s):  
D. M. Barnard ◽  
W. L. Bauerle

Abstract Characterization of seasonal dynamics in wind speed attenuation within a plant canopy α is necessary for modeling leaf boundary layer conductance , canopy–atmosphere coupling Ω, and transpiration at multiple scales. The goals of this study were to characterize seasonal variation in α in four tree species with canopy wind profiles and a canopy-structure model, to quantify the impact of α on estimates of and Ω, and to determine the influence of variable wind speed on transpiration estimates from a biophysical model [Multi-Array Evaporation Stand Tree Radiation Assemblage (MAESTRA)]. Among species, α varied significantly with above-canopy wind speed and seasonal canopy development. At the mean above-canopy wind speed (1.5 m s−1), α could be predicted using a linear model with leaf area index as the input variable (coefficient of determination R2 = 0.78). However, the canopy-structure model yielded improved predictions (R2 = 0.92) by including canopy height and leaf width. By midseason, increasing canopy leaf area and α resulted in lower within-canopy wind speeds, a decrease in by 20%–50%, and a peak in Ω. Testing a discrete increase in wind speed (0.6–2.4 m s−1; seasonal mean plus/minus one standard deviation) had variable influence on transpiration estimates (from −30% to +20%), which correlated strongly with vapor pressure deficit (R2 = 0.83). Given the importance of α in accurate representation of , Ω, and transpiration, it is concluded that α needs to be given special attention in plant canopies that undergo substantial seasonal changes, especially densely foliated canopies (i.e., leaf area index >1) and in areas with lower native wind speeds (i.e., <2 m s−1).


2019 ◽  
Author(s):  
Abbas Haghshenas ◽  
Yahya Emam ◽  
Ali Reza Sepaskhah ◽  
Mohsen Edalat

AbstractWheat cultivar mixtures with heterogeneous phenology has a less-explored potential to improve crop diversity, yield stability, and agronomic features particularly in response to the currently increased environmental stresses and uncertainties. To investigate the option of using wheat cultivar mixtures with different ripening patterns for mitigating the adverse effects of post-anthesis water stress, a two-year field experiment was conducted during 2014-15 and 2015-16 growing seasons at the research field of School of Agriculture, Shiraz University, Iran. The factorial experiment was a Randomized Complete Block Design with 3 replicates, in which 15 mixture treatments including monocultures and every 11 possible mixtures of four early- to middle-ripening wheat cultivars were grown under two normal and post-anthesis deficit-irrigation conditions. Measured traits and estimated indices included grain yield and its components, canopy temperature, soil water content, water productivity, susceptibility index, and water use efficiency. The results indicated that under the stressful condition of post-anthesis deficit-irrigation, heterogeneity in the ripening pattern of mixtures was declined. Consequently, dissimilarities in grain yields as well as various agronomic characters of mixture treatments were also lessened. This may be an evidence for the negative effect of water shortage stress on heterogeneity within agroecosystems. Although cultivar mixtures showed some casual advantages in some traits, such beneficial effects were not consistent across all conditions. Moreover, no cultivar mixture produced higher grain yield than the maximum monoculture. Despite the general expectation for beneficial ecological services from cultivar mixtures, in many cases disadvantageous blends were found which led to a considerable reduction in grain yield and water productivity. Therefore, it is suggested that unless the performance, and preferably the involved mechanisms, of cultivar mixtures are not fully understood, use of blends as an alternative for conventional high-input wheat cropping systems may lead to adverse results.


2021 ◽  
Vol 12 ◽  
Author(s):  
Peter M. Bourke ◽  
Jochem B. Evers ◽  
Piter Bijma ◽  
Dirk F. van Apeldoorn ◽  
Marinus J. M. Smulders ◽  
...  

Intercropping is both a well-established and yet novel agricultural practice, depending on one’s perspective. Such perspectives are principally governed by geographic location and whether monocultural practices predominate. Given the negative environmental effects of monoculture agriculture (loss of biodiversity, reliance on non-renewable inputs, soil degradation, etc.), there has been a renewed interest in cropping systems that can reduce the impact of modern agriculture while maintaining (or even increasing) yields. Intercropping is one of the most promising practices in this regard, yet faces a multitude of challenges if it is to compete with and ultimately replace the prevailing monocultural norm. These challenges include the necessity for more complex agricultural designs in space and time, bespoke machinery, and adapted crop cultivars. Plant breeding for monocultures has focused on maximizing yield in single-species stands, leading to highly productive yet specialized genotypes. However, indications suggest that these genotypes are not the best adapted to intercropping systems. Re-designing breeding programs to accommodate inter-specific interactions and compatibilities, with potentially multiple different intercropping partners, is certainly challenging, but recent technological advances offer novel solutions. We identify a number of such technology-driven directions, either ideotype-driven (i.e., “trait-based” breeding) or quantitative genetics-driven (i.e., “product-based” breeding). For ideotype breeding, plant growth modeling can help predict plant traits that affect both inter- and intraspecific interactions and their influence on crop performance. Quantitative breeding approaches, on the other hand, estimate breeding values of component crops without necessarily understanding the underlying mechanisms. We argue that a combined approach, for example, integrating plant growth modeling with genomic-assisted selection and indirect genetic effects, may offer the best chance to bridge the gap between current monoculture breeding programs and the more integrated and diverse breeding programs of the future.


2021 ◽  
Vol 16 (2) ◽  
pp. 147-154
Author(s):  
K. Ananthi ◽  
P. Parasuraman

Intercropping increases in productivity per unit of land via better utilisation of resources, minimises the risks, reduces weed competition and stabilizes the yield. Many intercropping systems have proved to be better than sole crops in terms of yield because intercropping makes better use of one or more agricultural resources both in time and in space. The beneficial effect of pulse crops is improving soil health in the form of biological nitrogen fixation, leaf fall, addition of considerable amount of organic matter through root biomass, improving microbial biomass and they keep soil productive and alive by bringing qualitative changes in physical, chemical and biological properties and sustaining productivity. The principal advantage of intercropping system is the more efficient utilization of soil, water, nutrient and increased productivity compared with each sole crop under rainfed and irrigated ecosystem. Choice of ecologically sound crops as millets and adoption of intercropping systems are two of suitable options for maximization of productivity in drylands cropping system due to the reason that competition of plant could be minimized not only by spatial arrangement, but also by combining those crops which have best able to exploit soil nutrients. A field study was scheduled to estimate the impact of intercropping varagu with greengram and blackgram cropping system under rain-fed situation onleaf area, leaf area index, specific leaf weight, crop growth rate, chlorophyll content, no. of tillers per plant and grain yield at Centre of Excellence in Millets, Athiyandal, Tiruvannamalai. It was done in Kharif, 2018 and 2019. Randomized Block Design was used to conduct this experiment. It has three replications. The aim of this study was to evaluate and compare varagu with blackgram and greengram inter cropping effects, as well as reveal which intercrops better adopts to rainfed cropping systems using these parameters to improve water use efficiency in the production. Highest returns were obtained from Sole Varagu with blackgram (1:1) due to greater productivity under this treatment with comparable cost of cultivation.


2013 ◽  
Vol 43 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Franklin B. Sullivan ◽  
Scott V. Ollinger ◽  
Mary E. Martin ◽  
Mark J. Ducey ◽  
Lucie C. Lepine ◽  
...  

Several recent studies have shown that the mass-based concentration of nitrogen in foliage (%N) is positively correlated with canopy near-infrared reflectance (NIRr) and midsummer shortwave albedo across North American forests. Understanding the mechanisms behind this relationship would aid in interpretation of remote sensing imagery and improve our ability to predict changes in reflectance under future environmental conditions. The purpose of this study was to investigate the extent to which foliar nitrogen at leaf and canopy scales covary with leaf- and canopy-scale structural traits that are known to influence NIR scattering and reflectance. To accomplish this, we compared leaf and canopy traits with reflectance spectra at 17 mixed temperate forest stands. We found significant positive associations among %N and NIRr at both the leaf and canopy scale. At the canopy scale, both %N and NIRr were correlated with a number of structural traits as well as with the proportional abundance of deciduous and evergreen foliage. Identifying specific causal factors for observed reflectance patterns was complicated by interrelations among multiple traits across scales. Among simple metrics of canopy structure, we saw no relationship between NIRr and leaf area index, but we observed a strong, inverse relationship with the number of leaves per unit canopy volume.


2019 ◽  
Vol 447 (1-2) ◽  
pp. 537-551 ◽  
Author(s):  
Iris Dahlin ◽  
Lars P. Kiær ◽  
Göran Bergkvist ◽  
Martin Weih ◽  
Velemir Ninkovic

Abstract Aims Cultivar mixtures can increase productivity through complementarity in resource use, but reported results are often conflicting and the role of plasticity in shaping plant-plant interactions is poorly understood. We aim to determine if individual cultivars show different phenotypic responses when grown in a mixture, whether these responses depend on the neighboring cultivar identity, and how they contribute to variations in productivity and nitrogen (N) use. Methods Five spring barley cultivars were field-grown in pure stands and in mixtures during 2 years. Plant traits related to development, growth, N use, and reproduction were measured to identify temporal patterns of plastic responses to neighboring plants. Results Plants in mixtures were shorter and developed slower early in the season, but later on they grew faster and produced more grain than the corresponding pure stands. Some cultivars showed complementary N accumulation only when grown together with specific neighbors. Mechanisms of improved productivity differed between the individual mixtures. Conclusions Plastic plant-plant interaction between cultivars is an important driver behind the variability in mixing effects. Results contribute to a better understanding of how productivity in cultivar mixtures is affected by plastic adaptation and differentiation of plant traits, depending on the environment created by neighboring genotypes.


2020 ◽  
Vol 126 (4) ◽  
pp. 671-685 ◽  
Author(s):  
Gaëtan Louarn ◽  
Romain Barillot ◽  
Didier Combes ◽  
Abraham Escobar-Gutiérrez

Abstract Backgrounds and Aims A major challenge when supporting the development of intercropping systems remains the design of efficient species mixtures. The ecological processes that sustain overyielding of legume-based mixtures compared to pure crops are well known, but their links to plant traits remain to be unravelled. A common assumption is that enhancing trait divergence among species for resource acquisition when assembling plant mixtures should increase species complementarity and improve community performance. Methods The Virtual Grassland model was used to assess how divergence in trait values between species on four physiological functions (namely light and mineral N acquisition, temporal development, and C–N use efficiency) affected overyielding and mixture stability in legume-based binary mixtures. A first step allowed us to identify the model parameters that were most important to interspecies competition. A second step involved testing the impact of convergent and divergent parameter (or trait) values between species on virtual mixture performance. Results Maximal overyielding was achieved in cases where trait values were divergent for the physiological functions controlling N acquisition and temporal development but convergent for light interception. It was also found that trait divergence should not affect competitive abilities of legume and non-legumes at random. Indeed, random trait combinations frequently led to reduced mixture yields when compared to a perfectly convergent neutral model. Combinations with the highest overyielding also tended to be associated with mixture instability and decreasing legume biomass proportion. Achieving both high overyielding and mixture stability was only found to be possible under low or moderate N levels, using combinations of traits adapted to each environment. Conclusions No simple assembly rule based on trait divergence could be confirmed. Plant models able to infer plant–plant interactions can be helpful for the identification of major interaction traits and the definition of ideotypes adapted to a targeted intercropping system.


2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Kelly A. Nelson ◽  
Clinton G. Meinhardt ◽  
Randall L. Smoot

Field research (2003–2005) evaluated the effect of wheat row spacing (19 and 38 cm) and cultivar on double-cropped (DC) soybean response, 38-cm wheat on relay-intercrop (RI) response, and wheat cultivar selection on gross margins of these cropping systems. Narrow-row wheat increased grain yield 460 kg , light interception (LI) 7%, and leaf area index (LAI) 0.5 compared to wide rows, but did not affect DC soybean yield. High yielding wheat (P25R37) with greater LI and LAI produced lower (330 kg ) soybean yields in an RI system than a low yielding cultivar (Ernie). Gross margins were 267  greater when P25R37 was RI with H431 Intellicoat (ITC) soybean compared to Ernie. Gross margins were similar for monocrop H431 non-coated (NC) or ITC soybean, P25R37 in 19- or 38-cm rows with DC H431 NC soybean, and P25R37 in 38-cm rows with RI H431 ITC soybean in the absence of an early fall frost.


2018 ◽  
Vol 10 (8) ◽  
pp. 1263 ◽  
Author(s):  
Alby Rocha ◽  
Thomas Groen ◽  
Andrew Skidmore ◽  
Roshanak Darvishzadeh ◽  
Louise Willemen

Spectral, temporal and spatial dimensions are difficult to model together when predicting in situ plant traits from remote sensing data. Therefore, machine learning algorithms solely based on spectral dimensions are often used as predictors, even when there is a strong effect of spatial or temporal autocorrelation in the data. A significant reduction in prediction accuracy is expected when algorithms are trained using a sequence in space or time that is unlikely to be observed again. The ensuing inability to generalise creates a necessity for ground-truth data for every new area or period, provoking the propagation of “single-use” models. This study assesses the impact of spatial autocorrelation on the generalisation of plant trait models predicted with hyperspectral data. Leaf Area Index (LAI) data generated at increasing levels of spatial dependency are used to simulate hyperspectral data using Radiative Transfer Models. Machine learning regressions to predict LAI at different levels of spatial dependency are then tuned (determining the optimum model complexity) using cross-validation as well as the NOIS method. The results show that cross-validated prediction accuracy tends to be overestimated when spatial structures present in the training data are fitted (or learned) by the model.


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