Lianhai Wu

Modelling root–soil interactions using three–dimensional models of root growth, architecture and function

BackgroundThree–dimensional root architectural models emerged in the late 1980s, providing an opportunity to conceptualise and investigate that all important part of plants that is typically hidden and difficult to measure and study. These models have progressed from representing pre–defined root architectural arrangements, to simulating root growth in response to heterogeneous soil environments. This was done through incorporating soil properties and more complete descriptions of plant function, moving into the realm of functional-structural plant modelling. Modelling studies are often designed to investigate the relationship between root architectural traits and root distribution in soil, and the spatio–temporal variability of resource supply. Modelling root systems presents an opportunity to investigate functional tradeoffs between foraging strategies (i.e. shallow vs deep rooting) for contrasting resources (immobile versus mobile resources), and their dependence on soil type, rainfall and other environmental conditions. The complexity of the interactions between root traits and environment emphasises the need for models in which traits and environmental conditions can be independently manipulated, unlike in the real world.ScopeWe provide an overview of the development of three–dimensional root architectural models from their origins, to their place today in the world of functional–structural plant modelling. The uses and capability of root architectural models to represent virtual plants and soil environment are addressed. We compare features of six current models, RootTyp, SimRoot, ROOTMAP, SPACSYS, R-SWMS, and RootBox, and discuss the future development of functional-structural root architectural modelling.ConclusionFunctional-structural root architectural models are being used to investigate numerous root–soil interactions, over a range of spatial scales. They are not only providing insights into the relationships between architecture, morphology and functional efficiency, but are also developing into tools that aid in the design of agricultural management schemes and in the selection of root traits for improving plant performance in specific environments.

Advances in the understanding of nutrient dynamics and management in UK agriculture

Current research on macronutrient cycling in UK agricultural systems aims to optimise soil and nutrient management for improved agricultural production and minimise effects on the environment and provision of ecosystem services. Nutrient use inefficiencies can cause environmental pollution through the release of greenhouse gases into the atmosphere and of soluble and particulate forms of N, P and carbon (C) in leachate and run-off into watercourses. Improving nutrient use efficiencies in agriculture calls for the development of sustainable nutrient management strategies: more efficient use of mineral fertilisers, increased recovery and recycling of waste nutrients, and, better exploitation of the substantial inorganic and organic reserves of nutrients in the soil. Long-term field experimentation in the UK has provided key knowledge of the main nutrient transformations in agricultural soils. Emerging analytical technologies, especially stable isotope labelling, that better characterise macronutrient forms and bioavailability and improve the quantification of the complex relationships between the macronutrients in soils at the molecular scale, are augmenting this knowledge by revealing the underlying processes. The challenge for the future is to determine the relationships between the dynamics of N, P and C across scales, which will require both new modelling approaches and integrated approaches to macronutrient cycling.

Models of biological nitrogen fixation of legumes. A review

Leguminous crops have the ability to fix nitrogen (N) biologically from the atmosphere. This can benefit not only the legumes themselves but also any intercropped or subsequent crops, thus reducing or removing the need to apply N fertilizers. Improved quantification of legume biological nitrogen fixation (BNF) will provide better guidance for farmers on managing N to optimise productivity and reduce harmful losses to the environment. There are many techniques available for the direct quantitative measurement of legume BNF in the field and in controlled environments. However, these are time-consuming and therefore expensive, and generate data relevant only to the time and place of measurement. Alternatively, legume BNF can be estimated by either empirical models or dynamic mechanistic simulation models. Comparatively, simulation by a dynamic model is preferable for quantifying legume BNF, because of its capability to simulate the response of N fixation to a wide range of environmental variables and legume growth status. Currently there is no published review of the approaches used to simulate, rather than measure, legume BNF. This review of peer-reviewed literature shows that most simulation models estimate the N fixation rate from a pre-defined potential N fixation rate, adjusted by the response functions of soil temperature, soil/plant water status, soil/plant N concentration, plant carbon (C) supply and crop growth stage. Here, we highlight and compare the methods used to estimate the potential N fixation rate, and the response functions to simulate legume BNF, in nine widely-cited models over the last 30 years.We then assess their relative strengths in simulating legume BNF with varying biotic and abiotic factors, and identify the discrepancies between experimental findings and simulations. After this comparison, we identify the areas where there is the potential to improve legume BNF simulation in the future. These include; (1) consideration of photosynthetic C supply, (2) refining the various effects of soil mineral N concentration, (3) characterization and incorporation of excess soil water stress and other factors into models, and (4) incorporation of the effects of grazing, coexistence and competition with intercrops and weeds into models to improve their practical relevance to sustainable agricultural systems. This review clarifies, for the first time, the current progress in legume BNF quantification in simulation models, and provides guidance for their further development, combining fundamental experimental and modelling work.

Developing Existing Plant Root System Architecture Models to Meet Future Agricultural Challenges

Improving our understanding of the relationships between soil conditions and plant growth, both above and below ground, will contribute to the development of cropping systems that are less reliant on mineral fertilizers for crop nutrition. Although many models predicting the flows of nutrients between plants and soil have been developed, few of these deal in detail with root architecture and dynamics. In this chapter, we review seven widely cited models of root architecture and development in terms of their ability to improve predictions of plant and soil nutrient flows. We have examined processes related to root system architecture and development, compared mathematical expressions and parameters used in the selected models, and summarized common processes and parameters for simulating root systems. This outcome should benefit researchers and model developers, preventing the need to spend limited resources on repeating the same process. Detailed conclusions include the fact that both inter-branching distance and insertion angle are essential parameters for representing root architecture. Additionally, in a three-dimensional model an extra parameter, radial angle, should be used for determining the location of a branch relative to the root from which it originated. Root growth is simulated by elongation rate and elongation direction, with root component diameter also represented in some models. Almost all the three-dimensional models reviewed calculate the current direction of newly formed root segments using the previous direction of tip extension together with an angle related to geotropism. This review was carried out as the first stage in a research program on integrating root growth models with soil nutrient cycling models. For this purpose, the review suggests that, in order to optimize practical applications of these models in cropping systems, there is a need to integrate a number of additional processes, including root longevity and mortality, environmental responses, and effects of management such as tillage or the pesticide application regime. The form of root mortality relevant to nutrient cycling in soil is that due to natural senescence of root components. This differs from catastrophic death of roots due to attack by pathogenic fungi, which has been considered in one existing root model. To achieve the required objectives, there is also a need to strengthen the integration of above-ground plant component dynamics with root system development, particularly in relation to breeding new crop varieties for sustainable agricultural systems.

Models of Biological Nitrogen Fixation of Legumes

Leguminous crops have the ability to fix nitrogen (N) biologically from the atmosphere. This can benefit not only the legumes themselves but also any intercropped or subsequent crops, thus reducing or removing the need to apply N fertilizers. Improved quantification of legume biological nitrogen fixation (BNF) will provide better guidance for farmers on managing N to optimise productivity and reduce harmful losses to the environment. There are many techniques available for the direct quantitative measurement of legume BNF in the field and in controlled environments. However, these are time-consuming and therefore expensive, and generate data relevant only to the time and place of measurement. Alternatively, legume BNF can be estimated by either empirical models or dynamic mechanistic simulation models. Comparatively, simulation by a dynamic model is preferable for quantifying legume BNF, because of its capability to simulate the response of N fixation to a wide range of environmental variables and legume growth status. Currently there is no published review of the approaches used to simulate, rather than measure, legume BNF. This review of peer-reviewed literature shows that most simulation models estimate the N fixation rate from a pre-defined potential N fixation rate, adjusted by the response functions of soil temperature, soil/plant water status, soil/plant N concentration, plant carbon (C) supply and crop growth stage. Here, we highlight and compare the methods used to estimate the potential N fixation rate, and the response functions to simulate legume BNF, in nine widely-cited models over the last 30 years. We then assess their relative strengths in simulating legume BNF with varying biotic and abiotic factors, and identify the discrepancies between experimental findings and simulations. After this comparison, we identify the areas where there is the potential to improve legume BNF simulation in the future. These include; (1) consideration of photosynthetic C supply, (2) refining the various effects of soil mineral N concentration, (3) characterization and incorporation of excess soil water stress and other factors into models, and (4) incorporation of the effects of grazing, coexistence and competition with intercrops and weeds into models to improve their practical relevance to sustainable agricultural systems. This review clarifies, for the first time, the current progress in legume BNF quantification in simulation models, and provides guidance for their further development, combining fundamental experimental and modelling work.

Carbon sequestration by fruit trees--Chinese apple orchards as an example.

Apple production systems are an important component in the Chinese agricultural sector with 1.99 million ha plantation. The orchards in China could play an important role in the carbon (C) cycle of terrestrial ecosystems and contribute to C sequestration. The carbon sequestration capability in apple orchards was analyzed through identifying a set of potential assessment factors and their weighting factors determined by a field model study and literature. The dynamics of the net C sink in apple orchards in China was estimated based on the apple orchard inventory data from 1990s and the capability analysis. The field study showed that the trees reached the peak of C sequestration capability when they were 18 years old, and then the capability began to decline with age. Carbon emission derived from management practices would not be compensated through C storage in apple trees before reaching the mature stage. The net C sink in apple orchards in China ranged from 14 to 32 Tg C, and C storage in biomass from 230 to 475 Tg C between 1990 and 2010. The estimated net C sequestration in Chinese apple orchards from 1990 to 2010 was equal to 4.5% of the total net C sink in the terrestrial ecosystems in China. Therefore, apple production systems can be potentially considered as C sinks excluding the energy associated with fruit production in addition to provide fruits.

The North Wyke Farm Platform: effect of temperate grassland farming systems on soil moisture contents, runoff and associated water quality dynamics

Summary The North Wyke Farm Platform was established as a United Kingdom national capability for collaborative research, training and knowledge exchange in agro‐environmental sciences. Its remit is to research agricultural productivity and ecosystem responses to different management practices for beef and sheep production in lowland grasslands. A system based on permanent pasture was implemented on three 21‐ha farmlets to obtain baseline data on hydrology, nutrient cycling and productivity for 2 years. Since then two farmlets have been modified by either (i) planned reseeding with grasses that have been bred for enhanced sugar content or deep‐rooting traits or (ii) sowing grass and legume mixtures to reduce nitrogen fertilizer inputs. The quantities of nutrients that enter, cycle within and leave the farmlets were evaluated with data recorded from sensor technologies coupled with more traditional field study methods. We demonstrate the potential of the farm platform approach with a case study in which we investigate the effects of the weather, field topography and farm management activity on surface runoff and associated pollutant or nutrient loss from soil. We have the opportunity to do a full nutrient cycling analysis, taking account of nutrient transformations in soil, and flows to water and losses to air. The NWFP monitoring system is unique in both scale and scope for a managed land‐based capability that brings together several technologies that allow the effect of temperate grassland farming systems on soil moisture levels, runoff and associated water quality dynamics to be studied in detail. Highlights Can meat production systems be developed that are productive yet minimize losses to the environment? The data are from an intensively instrumented capability, which is globally unique and topical. We use sensing technologies and surveys to show the effect of pasture renewal on nutrient losses. Platforms provide evidence of the effect of meteorology, topography and farm activity on nutrient loss.

Simulation of nitrous oxide emissions at field scale using the SPACSYS model.

Nitrous oxide emitted to the atmosphere via the soil processes of nitrification and denitrification plays an important role in the greenhouse gas balance of the atmosphere and is involved in the destruction of stratospheric ozone. These processes are controlled by biological, physical and chemical factors such as growth and activity of microbes, nitrogen availability, soil temperature and water availability. A comprehensive understanding of these processes embodied in an appropriate model can help develop agricultural mitigation strategies to reduce greenhouse gas emissions, and help with estimating emissions at landscape and regional scales. A detailed module to describe the denitrification and nitrification processes and nitrogenous gas emissions was incorporated into the SPACSYS model to replace an earlier module that used a simplified first-order equation to estimate denitrification and was unable to distinguish the emissions of individual nitrogenous gases. A dataset derived from a Scottish grassland experiment in silage production was used to validate soil moisture in the top 10 cm soil, cut biomass, nitrogen offtake and N2O emissions. The comparison between the simulated and observed data suggested that the new module can provide a good representation of these processes and improve prediction of N2O emissions. The model provides an opportunity to estimate gaseous N emissions under a wide range of management scenarios in agriculture, and synthesises our understanding of the interaction and regulation of the processes.

A Review of Quantitative Tools for Assessing the Diffuse Pollution Response to Farmer Adaptations and Mitigation Methods Under Climate Change

In an era of global climate change, the agricultural sector faces the challenge of increasing the production of safe and nutritious food supplies to meet a growing world population while safeguarding the environment. Farmers will adapt their agricultural practices to a changing climate to safeguard against loss of production and to take advantage of any positive climatic conditions. Certain management practices have been found to reduce the effects of agricultural practices on the environment and a key question is how efficient these are under the current climate, and will these management practices still be relevant under a changing climate? Mathematical modeling is the only tool available to assess the potential efficacy of proposed agricultural management practices to help evaluate their impacts on the environment in a future climate. This chapter attempts to evaluate a range of published models for their capability to simulate agricultural production systems and associated environmental system losses under a changing climate, and their ability to introduce farmer adaptation and mitigation methods. The chapter focuses on the applicability of the models given a set of essential criteria related to scale, biophysical processes, and land management. Thirty models are initially examined, based on details found in published papers, against specific criteria, viz: (1) spatial scale and temporal scale, ease of use, and ability to consider a change in climate; (2) ability to simulate nutrient cycling processes, specifically carbon and nitrogen dynamics with microbial turnover, mineralization–immobilization, nitrification and denitrification, plant nutrient uptake, and phosphorus cycling; (3) ability to consider a water balance and water movement through soil; and (4) ability to introduce and modify agricultural practices relating to crop and livestock management. The chapter does not compare any actual model simulations. It was concluded that albeit no single model incorporates all above stated requirements, there were three models, DAYCENT, PASIM, and SPACSYS which will accommodate most features. These models may therefore be considered in the context of this chapter to be the most suitable for a general assessment of the effects of farm mitigation and adaptation on environmental losses under a changing climate.

Higher yields and lower methane emissions with new rice cultivars

Breeding high-yielding rice cultivars through increasing biomass is a key strategy to meet rising global food demands. Yet, increasing rice growth can stimulate methane (CH4 ) emissions, exacerbating global climate change, as rice cultivation is a major source of this powerful greenhouse gas. Here, we show in a series of experiments that high-yielding rice cultivars actually reduce CH4 emissions from typical paddy soils. Averaged across 33 rice cultivars, a biomass increase of 10% resulted in a 10.3% decrease in CH4 emissions in a soil with a high carbon (C) content. Compared to a low-yielding cultivar, a high-yielding cultivar significantly increased root porosity and the abundance of methane-consuming microorganisms, suggesting that the larger and more porous root systems of high-yielding cultivars facilitated CH4 oxidation by promoting O2 transport to soils. Our results were further supported by a meta-analysis, showing that high-yielding rice cultivars strongly decrease CH4 emissions from paddy soils with high organic C contents. Based on our results, increasing rice biomass by 10% could reduce annual CH4 emissions from Chinese rice agriculture by 7.1%. Our findings suggest that modern rice breeding strategies for high-yielding cultivars can substantially mitigate paddy CH4 emission in China and other rice growing regions.