Elsevier

Field Crops Research

Volume 257, 15 October 2020, 107931
Field Crops Research

The contribution of spike photosynthesis to wheat yield needs to be considered in process-based crop models

https://doi.org/10.1016/j.fcr.2020.107931Get rights and content

Highlights

  • Contribution of spikes photosynthesis to grain yield of wheat was 20%.

  • No current models explicitly consider contribution of spike photosynthesis to grain.

  • Simulation results of excluding & including spike contributions are investigated.

  • APSIM overestimated leaf contribution to compensate neglection of spike contribution.

  • Most current models may simulate wheat yield correctly partly for the wrong reasons.

Abstract

Process-based crop models are increasingly used to simulate Genotype by Environment by Management interactions (GxExM), and to evaluate importance of physiological traits to assist in breeding selections. This requires the model to correctly simulate the key physiological processes that determine grain yield. Although spike photosynthesis contributes to grain yield of wheat, it has not been explicitly simulated in most crop models. Here we present experimental data from China and Mexico, derived from estimates of spike contributions to grain yield of wheat, and compared them with previous results. We then used the APSIM model to investigate the consequences of excluding and including spike contributions on simulation results, against the data from China consisting of 4 irrigation and plant density treatments. Our results show that the contribution of spikes photosynthesis to grain yield ranged from 9.8% to 39.0% with an average of 20.1%, consistent with results from previous studies. However, despite the omission of the contribution from spike photosynthesis to grain yield, APSIM captured the dynamics of LAI and biomass and grain yield with acceptable accuracy. Across the treatments, the APSIM (version 7.9) explained >78% of the variation in yield (RMSE = 737 kg ha-1) and biomass (RMSE = 1438 kg ha-1) compared with the experimental data. This highlight the fact that APSIM performed well in simulating yield partly for the wrong reasons. Adding spike contribution to APSIM improved the simulations of biomass and yield, particularly under high yield level. More detailed analysis revealed that APSIM overestimated leaf contribution to compensate neglection of spike contribution, explained by the overestimation of leave biomass and underestimation of stem biomass. While APSIM captured the trend of changes in pre-anthesis remobilization contribution to grain yield, it also underestimated this contribution across treatments. Future improvements should include the inclusion of spike photosynthesis contribution with correct spike light interception and RUE, and improved modelling of biomass partitioning to different organs and the remobilization of biomass to grain from pre-anthesis growth.

Introduction

Wheat leaves are the key organs for photosynthesis assimilate production. However, spikes possess chlorophyll and photosynthetic functions, and also play an important role in grain yield production. The importance of spike photosynthesis has been reported due to delayed spike senescence (Abbad et al., 2004), better photosynthetic performances compared to leaves (Tambussi et al., 2005) under water stress conditions, thus the capacity to secure grain-filling under source limitations (Maydup et al., 2010). Moreover, spike partially refixes respiratory CO2 by grain respiration during grain filling, thereby reduces respiratory loss, increasing the transpiration efficiency of the organ (Araus et al., 1993; Bort et al., 1994). However, the contribution of spikes photosynthesis to grain yield was found to vary widely, i.e. between 10% and 76%, as a result of variations in environmental and genotypic factors, as well as differences in methodologies used to measure the spike photosynthesis contribution.

Most of the methodologies designed to estimate the photosynthetic contribution of different plant parts (e.g. spikes) to filling grains have involved intrusive approaches based on differential prevention of organ photosynthesis. Such approaches include shading the spikes (Buttrose and May, 1959; Thorne, 1963), application of photosynthesis inhibitor such as DCMU (3'-(3,4-dichlorophenyl)-1',1'-dimethylurea) (Maydup et al., 2010; Sanchez-Bragado et al., 2016), or defoliating leaf blades (Aggarwal et al., 1990; Ahmadi et al., 2009). Another widely used method relies on the stable carbon isotope signature or in its natural abundance (Lupton, 1969; Sanchez-Bragado et al., 2014a, 2014b).

Shading techniques have been used by many investigators since they were first introduced (Boonstra, 1929). It has been generally recognized that the shading technique is open to criticism for spike-shading material changed the microenvironment and reflection characteristics of the spike surface(Kriedemann, 1966). Besides the intrusive nature of these techniques, compensation effects triggered by shading treatments may cause increase in the photosynthetic rate of the unshaded green organs. The DCMU approach as an improvement of shading techniques are also subject to compensatory effects, tended to overestimate the contribution of unaffected organs, but it has little effect on light interception of other organs. The above methods have the advantages of being simple and relatively low in cost. A complementary method used to estimate the photosynthetic capacities of the spike is to use carbon isotope signature. One such method is using 14CO2 tracer where labelled CO2 is ‘fed’ to spike and its 14C activity per unit weight can be compared with the grain total fixation of 14CO2 as measures of photosynthesis (Lupton, 1969; Bremner and Rawson, 1972). This non-intrusive mothed is more reliable but relatively complex and laborious. Another less time-consuming alternative is to measure the stable carbon isotope signature in its natural abundance to elucidate the relative contribution of the different photosynthetic organs (Sanchez-Bragado et al., 2014a, 2014b). This method compares the δ13C of total dry matter in mature kernels and that of the water-soluble fractions (WSF) of the flag leaf and the spikes during grain filling, to determine the relative contribution of flag leaf and spike photosynthesis. The results from the δ13C method showed that the contribution of the spike photosynthesis was on average about 70% of the total assimilates contributed to grain. Such results may be subject to great uncertainty due to the fact that the observed 13C enrichment could be the consequence of secondary fractionation during remobilization and storage of carbohydrates (Araus et al., 1992; Araus et al., 1993).

A review on the contribution of spike photosynthesis to grain yield revealed a very wide range depending on the methods used. Source of variation first comes from genotypic difference (e.g. awned vs. awnless) and then management (e.g. irrigation) (Table 1). In spite of the variability, results from all different methods support the importance of the spike as a major photosynthesis organ contributing to grain yield.

Process-based modelling of crop growth is an effective way of representing how crop genotype, environment, and management interactions affect crop production to aid tactical and strategic decision making (Holzworth et al., 2015; Zhao et al., 2015; Wang et al., 2017; Wang et al., 2019; Zhao et al., 2019). Process-based crop models are increasingly used to better capture Genotype by Environment by Management (GxExM) interactions as well as to project the impact of climate change on crop yield. Currently, there are more than 30 widely used wheat models (Wang et al., 2017).

Given that spike photosynthesis is a significant source of grain carbohydrate, it should be part of any crop model that simulates wheat yield. While crop models have been frequently applied to simulate wheat for GxExM interactions, none of the current models (to our best knowledge) explicitly consider the contribution of spike photosynthesis to grain. They normally assume that only the leaf organ intercepts the radiation for biomass production, neglecting spike photosynthesis or simulating an overall assimilation rate by a green area of plant. Some of the models simulate photosynthesis and respiration separately for biomass growth, while others use a simple radiation use efficiency (RUE) approach to convert light interception to biomass growth. Spikes contribution to grain yield due to genotypic and environmental variations cannot be evaluated using such models that do not consider the contribution of spike photosynthesis to grain. In addition, the impact of genotypic changes on photosynthesis capacity and radiation use efficiency (RUE) have been seldom evaluated, with little or no improvement in the modelling of the contribution of different plant organs to final crop yield.

In this paper, we aim to: i) present additional experimental data on spike photosynthesis and its contribution to grain yield; ii) compare them with the findings in several other studies, iii) evaluate the performance of APSIM-wheat model for simulations of wheat biomass growth and grain yield, and iv) discuss potential issues in APSIM for simulating contribution of spikes and leaves to grain yield and future improvement needed. The reason for choosing APSIM is that APSIM-Wheat model is one of the widely used models (Wang et al., 2017; Holzworth et al., 2018; Wang et al., 2004), and has been frequently applied in North China Plain (where our experimental site is located) for studying crop productivity and farming systems performances (Sun et al., 2015; Wang et al., 2014; Zhang et al., 2012; Zhao et al., 2015; Zhao et al., 2017; Zhao et al., 2014a; Zhao et al., 2014b). Most data under i) are from various field experiments in North China Plain, but they are supplemented by a hitherto largely unpublished and unique experiment from northwest Mexico, unique since it exposed whole wheat canopies to 14CO2 labelled CO2, something apparently never reported before in the literature.

Section snippets

Field experiments in North China Plain

The field experiments were conducted during 2015–2017 at Experimental Station of China Agricultural University at Wuqiao, Hebei Province (37°69′ N, 116°62′ E, 18 m above sea level) in North China Plain. The study site is characterized with a summer monsoon climate with annual precipitation of 562 mm (Fig. 1). About 70% of the annual precipitation fall between July and September (Zhao et al., 2014b). The typical cropping system is winter wheat and summer maize double cropping rotation system (

Experimental Results from North China Plain

Table 4 shows the grain yield and maximum LAI from different irrigation (W) and sowing density (SD) treatments. Analysis of variance (ANOVA) indicated both irrigation and sowing density had significant effect on the grain yield and maximum LAI (P < 0.001), whereas no significant effect from interactions of irrigation and sowing density were found.

Spikes shading after anthesis reduced grain yield significantly (P < 0.05) in both irrigation and sowing density treatments (Fig. 2a-d). The

Discussion

This study provides additional experimental data on contribution of wheat spike photosynthesis to grain yield, and analysed the impact of ignoring and including spike contribution in crop modelling with APSIM. These data were measured using three different methods, i.e., shading, defoliating and 14C isotope. Discussions on advantages and disadvantages of different methods can be found in previous studies and reviews (Maydup et al., 2010; Sanchez-Bragado et al., 2016; Aggarwal et al., 1990;

Conclusion

Our results further confirm those from previous studies that spike photosynthesis contributes 9.8% to 39.0% to grain biomass in wheat with an average of 20.1%. The results from APSIM modelling demonstrate that most current crop models that do not explicitly consider spike contribution may simulate wheat yield correctly partly for the wrong reasons. Spike light interception and contribution to grain biomass growth need to be included if process-based crop models are used to simulate key

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Meng Zhang: Methodology, Software, Writing - original draft. Yanmei Gao: Investigation, Resources, Formal analysis. Yinghua Zhang: Writing - review & editing. Tony Fischer: Investigation, Writing - review & editing. Zhigan Zhao: Conceptualization, Supervision, Software, Writing - review & editing. Xiaonan Zhou: Validation. Zhimin Wang: Writing - review & editing, Supervision. Enli Wang: Conceptualization, Writing - review & editing.

Declaration of Competing Interest

The authors report no declarations of interest.

Acknowledgment

We gratefully acknowledge the financial support from the National Key Research and Development Program of China (2016YFD0300401), and the Earmarked Fund for Modern Agro-Industry Technology Research System (CARS-3), and the CSIRO and Chinese Academy of Agricultural Sciences (CAAS) through the research project ‘Scientific benchmarks for sustainable agricultural intensification’, and the Australia-China Joint Research Centre– Healthy Soils for Sustainable Food Production and Environmental Quality (

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