Correspondence: Emma W. Littleton (email@example.com) Received: 20 June 2019 – Discussion started: 13 August 2019
Revised: 17 January 2020 – Accepted: 23 January 2020 – Published: 11 March 2020
Abstract. We describe developments to the land surface model JULES, allowing for flexible user-prescribed harvest regimes of various perennial bioenergycrops or natural veg- etation types. Our aim is to integrate the most useful aspects of dedicated bioenergy models into dynamic global vegeta- tion models, in order that assessment of bioenergy options can benefit from state-of-the-art Earth system modelling. A new plant functional type (PFT) representing Miscanthus is also presented. The Miscanthus PFT fits well with growth parameters observed at a site in Lincolnshire, UK; however, global observed yields of Miscanthus are far more variable than is captured by the model, primarily owing to the model’s lack of representation of crop age and establishment time. Global expansion of bioenergy crop areas under a 2 ◦ C emis- sions scenario and balanced greenhouse gas mitigation strat- egy from the IMAGE integrated assessment model (RCP2.6- SSP2) achieves a mean yield of 4.3 billion tonnes of dry matter per year over 2040–2099, around 30 % higher than the biomass availability projected by IMAGE. In addition to perennial grasses, JULES-BE can also be used to represent short-rotation coppicing, residue harvesting from cropland or forestry and rotation forestry.
2 Surface fluxes and energy balance
The surface fluxes of heat, moisture and momentum are cal- culated in JULES within the surface exchange module. To
give the maximum flexibility in terms of the representation of surface heterogeneity and for the coupling of the land surface scheme to an atmospheric model, two generic types of sur- face are considered; vegetated and non-vegetated. The main difference between these two types of surface is the way in which the surface related parameters (e.g., albedo, roughness length) are specified. For non-vegetative surfaces they are specified by the user (with the exception of the MORUSES option for an urban surface, see Sect. 2.4), whereas for vege- tated surfaces these parameters are derived from the structure of the vegetation itself. This leads to an alternative set of pa- rameters that needs to be specified (e.g., rate of change of surface albedo with leaf area index, rate of change of rough- ness length with canopy height).
LAI values are shown in Fig. 7 for four major crop producing countries. To produce the country averages, grid cell LAI are combined by weighting by the grid cell contribution to total country crop area. In the USA and China each crop growing season occupies the similar set of summer months, whereas for India and Brazil the wheat cropping season is distinct from the other three crops. Peak LAI is greatest in Brazil and lowest in China, which is most likely a reflection of the ab- sence of irrigation in the model and the relative abundance of rainfall in each country. In comparison to the standard JULES configuration the addition of crops adds a season- ality to LAI as there is no default seasonality to vegetation characteristics in JULES. The annual variation of crop LAI is dampened when aggregated with the other plant functional types, which explains the non-zero LAI in the non-growing season in the JULES-crop simulation. Figure 7 shows that the inclusion of crops alters the grid box net primary produc- tion (NPP) in terms of the timing of peak fluxes. There are also lower fluxes in winter due to the more realistic treatment of LAI at this time. Therefore, including a representation of crops in JULES may help improve the seasonality of LAI, which affects carbon fluxes.
Acknowledgements. We gratefully acknowledge all funding bodies. AH was funded by the NERC Joint Weather and Climate Research Programme and NERC grant NE/K016016/1. The study has been supported by the TRY initiative on plant traits (http://www.try-db.org). The TRY initiative and database is hosted, developed, and maintained by J. Kattge and G. Bönisch (Max Planck Institute for Biogeochemistry, Jena, Germany). TRY is currently supported by DIVERSITAS/Future Earth and the German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig. O. K. Atkin acknowledges the support of the Australian Research Council (CE140100008). Met Office authors were supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). V. Onipchenko was supported by RSF (RNF) (project 14-50-00029). J. Peñuelas acknowledges support from the European Research Council Synergy grant ERC- SyG-2013-610028, IMBALANCE-P, and ÜN from the advanced grant ERC-AdG-322603, SIP-VOL + . We also thank Andrew Hartley (UK Met Office), who processed the ESA Land Cover data to the 5 and nine PFTs, and Nicolas Viovy (IPSL-LSCE), who kindly provided the CRU-NCEP driving data.
Like most other land surface models, JULES uses a tiled land surface scheme to represent heterogeneity in land cover. Many land models have fixed descriptions of the surface types that are designed with specific applications in mind. However, the flexible structure within JULES enables the de- scription of the resolved surface types to be targeted for spe- cific applications. This means, for instance, that there can be a small number of vegetation types for weather forecast- ing applications where computation cost is critical, but many vegetation types for climate modelling where an accurate representation of the various biomes is important. In addi- tion, JULES introduces elevation bands to the surface types, which is not common in land surface models. The elevated surfaces enable a modified surface energy balance which can be critical for the evolution of snowmelt and sublimation.
Here we have used the ESA CCI land cover product as our observational data for comparison with the model output. The CCI product has been translated into the five PFTs that are used in JULES (Poulter et al., 2015), and through the process of data collection and classification, a number of un- certainties are introduced which result in a range of possible outcomes for land cover distribution (Hartley et al., 2017). These uncertainties can include variation in classifying the surface reflectance products into the 22 land cover classes and aggregating these by dominant vegetation type into just five PFTs for JULES using a consultative cross-walking tech- nique. This classification also takes into account seasonal variation in normalised difference vegetation index (NDVI; greenness), burnt area, cloud cover and snow occurrence that can all vary throughout the year, giving a large range between the minimum and maximum possible vegetation cover for any one PFT, as shown in Fig. 4 and the blue bars in Fig. 5. For this reason we also use the MODIS VCF for benchmark- ing comparison. The VCF product is a characterisation of the land surface into just three components of ground cover us- ing satellite data: tree cover, non-tree vegetation cover and bare ground. The model performs well compared to a simple classification of tree and non-tree vegetation cover, showing that the spatial coverage of vegetation is simulated well when both disturbances are added to JULES. The benchmarking results compared to CCI still show an improvement com- pared to the control, but on a global scale this is better when each disturbance is considered separately, suggesting further parameterisation may be beneficial for each PFT. However, it is important to consider regional improvements or degra- dation as well, which can be masked in global scale analy- ses (Figs. 1, 3, 5). It also suggests that there may be some overlap in the disturbances, which reflects the complicated nature of how fire and LULCC are often used together for land clearance, and future development would benefit from reducing burnt area in cropland areas (Bistinas et al., 2014). The HYDE LULCC dataset in this study has been devel- oped from a combination of model, satellite and historical reconstructions of agricultural and population data, and the
used an earlier version of JULES than that described here (version 2.1.2 rather than 2.2). This later version includes representation of the effects of ozone on leaf physiology, fur- ther options for the calculation of canopy photosynthesis (op- tions 4 and 5 in Table 3), the ability to disable competition between vegetation types in the TRIFFID dynamic vegeta- tion model, and options for a more advanced treatment of ur- ban areas (see Part 1), in addition to bug fixes. The latest ver- sion of JULES is version 3.0 in which the land surface model can be coupled to the IMOGEN system (Huntingford et al., 2010), thereby allowing a first-order assessment of how the biogeochemical processes represented in JULES might re- spond to, and in turn feed back on, a changing climate. The details of version 3.0 are beyond the scope of this paper.
The Gallagher report (Gallagher, 2008) eloquently identifies the indirect effects on land use change, that results from displaced agricultural production, of biofuels production. The report, however, does not mention freight transport of biomass as representing an essential input for any biomass national deployment programme that requires careful planning if it is to be truly sustainable. The same report focuses on the indirect land use impacts of biofuels production on agricultural land (not biomass per se). The Gallagher review, however, does point out the need to enforce sustainability criteria of biofuels/biomass deployment within the EU Renewable Energy Directive.
There is another ORCHIDEE version including short- rotation coppice poplar plantations (ORCHIDEE-SRC; Fig. S1 in the Supplement, De Groote et al., 2015) based on the forest management module (Bellassen et al., 2010), but ORCHIDEE-SRC is more designed for studying spe- cific coppicing processes and is evaluated using only two coppicing sites in Belgium. Although detailed forest man- agement processes are not included in ORCHIDEE-MICT, this version includes explicit gross land use changes, i.e., the rotational transitions from other vegetation types to woody bioenergycrops and periodic clear-cut harvest of forests. These features are important to study the carbon emissions from bioenergycrops when their areas expand by convert- ing other land use types in future BECCS scenarios. In addi- tion, ORCHIDEE-MICT contains a bookkeeping system to track different forest age classes as separate land cohorts at a sub-grid scale (Yue et al., 2018). This functionality allows for the simulation of woody harvest based on rotation length tracking the carbon stock dynamics of different age classes of forests individually. In addition to the poplar plantation in Europe in ORCHIDEE-SRC (De Groote et al., 2015), we aimed to include herbaceous bioenergycrops like Miscant- hus and switchgrass as well as other woody crops like eu- calypt and willow in a more systematic way on the global scale.
The Bus topology is one of the fundamental structures of Local Area Network (LAN), which was used in a limited area with a limited number of nodes. In the Earlier days the topology used to be created with a limited number of nodes. Now a day it has changed to more number of nodes to be connected to a LAN Environment. Bus topology is a topology which broadcasts the message among the number of users. In bus topology, each user is connected to an Ethernet Coaxial Cable which is also known as a Backbone. Here the process of data transmission between 10 number of nodes has been simulated and the results are evaluated through the simulation. According to the nature of Bus topology, there may be many chances of network congestion creation as the Bus topology transmits the data among every user. When it finds the T connection while passing through the backbone the message is broadcasted. There are terminators at the both ends of the topology which defines the end of the topology. Bus topology is not expensive, but to add a node into the topology is easy too. To add a new connection the network needs to be off.
The main objective of CROPS is to develop a highly configurable, modular and clever carrier platform (Fig. 1) comprising a carrier plus modular parallel manipulators and “intelligent tools” (sensors, algorithms, sprayers, grippers) that can easily be installed onto the carrier and that are capable of adapting to new tasks and conditions. Both the scientific know-how and a number of technological demonstrators will be developed for the agro management of high value crops like greenhouse
Findings and Conclusions: The objective of this study was to parameterize the AquaCrop model for two bioenergycrops, switchgrass and forage sorghum, using field measurements from Stillwater, Oklahoma in 2011. The parameterized model was then validated for additional sites at Chickasha and Woodward, Oklahoma. After parameterization at Stillwater, the simulated canopy cover closely matched the measured canopy cover dynamics with a RMSE of 6% in switchgrass and 5% in forage sorghum. The water stress thresholds for canopy expansion and stomatal conductance were similar for switchgrass and forage sorghum, but senescence was induced at 35% available water depletion for forage sorghum compared to 85% for switchgrass. The maximum rooting depth of switchgrass was estimated at 190 cm and that of forage sorghum at 120 cm. The normalized water productivity of switchgrass was found to be 14 g m -2 , approximately half that of forage sorghum which was 27 g m -2 . The parameterized model reasonably simulated soil water depletion at Stillwater (RMSE < 34 mm) and canopy cover at Chickasha and Woodward (RMSE < 11%) for both crops. This calibrated model was then used to predict ethanol yields as a simulation study at Goodwell, Oklahoma. The corn, forage sorghum and switchgrass were simulated using AquaCrop five water levels: rainfed with initial soil moisture conditions of 60% available water capacity, 80% available water capacity, 100% available water capacity, and irrigation treatments at 70% allowable depletion, and at 50% allowable depletion. The simulation study was done over a period of ten years 2002-2011 to assess the long term performance. County average yields were consistent with simulated grain yields for corn under irrigated and rainfed conditions. Forage sorghum produced 30 % higher theoretical ethanol yields than corn under irrigated environments but not under rainfed environments. Switchgrass did not produce
V predchádzajúcej kapitole bol vybraný finálny variant, ktorý bude následne podrobne rozpísaný. Celé zariadenie je rozdelené do piatich základných častí. Medzi nich patrí najkomplikovanejšia, ale aj najdôleţitejšia časť stroja - vrchná mechanická časť (1). V tejto sekcii sa nachádzajú loţiská, motor, prevodovka, hriadele a iné. Spodná mechanická časť (2) obsahuje optickú aparatúru a všetko okolo nej. V tretej časť s názvom Pneumatický systém a PLC riadiaca jednotka (3) je popísaná riadiaca jednotka a rozvod vzduchového média do systému. Štvrtá časť obsahuje popis rámovej konštrukcie (4). Posledná konštrukčná časť zahŕňa rozvodovú skriňu (4). V tejto kapitole sa opisuje celá elektroinštalácia simulátora od frekvenčného meniča aţ po hlavné vypínacie tlačidlo. Po opise konštrukčnej časti zariadenia nasleduje samotná cenová rozvaha celej diplomovej práce. V poslednej časti je opísané samotné úvodné testovacie meranie, ktoré sa uskutočnilo na zhotovenom simulátore. V kapitole Konštrukčné riešenie je detailne popísaná príprava, postup a samotný výsledok tohto merania.
Because the government proposed that 30% of the petroleum used in the US be replaced with renewable biofuels by 2030, the production of lignocellulosic bioenergy feedstocks is expected to increase (Milliken et al., 2007; Heaton et al., 2008). To reach this goal, the production of bioenergycrops will require increased production area, directly affecting land availability for premium food crops (Gallagher, 2008). Identifying and utilizing land areas that are not suitable for row crops, generally termed as marginal lands (Nelson et al., 1997), to produce bioenergycrops can reduce competition between food and bioenergycrops for prime agricultural land (Gallagher, 2008; Tilman et al., 2009). These marginal lands can have unsuitable physical characteristics in a poor climate, and can also include high salinity areas; waterlogged, marshy lands; or barren and glacial areas (Milbrandt & Overend, 2009), as well as be degraded and abandoned agricultural lands (Tilman et al., 2009; Shortall, 2013).
1.2 Thesis overview and Research Objectives
Current Land Surface Models, such as JULES, are complex in terms of all the processes that they include. This complexity is reflected by the number of model parameters, some of which cannot be measured directly at the relevant spatial scales (Demaria et al., 2007). Hence, a sensitivity analysis of model parameters was carried out in order to test the hypothesis of whether there are some specific parameters or parameter groups that have a substantial effect on model outputs. A Monte Carlo Sensitivity analysis was implemented at the point scale (the scale at which the equations central to JULES are theoretically most applicable) using data from the Warren Farm recharge site, a chalk location, located in the Lambourn catchment, a tributary of the Kennet. For larger scale modelling (i.e. scales larger than 1 km 2 ) soil data from the National Soil Resources Institute (NSRI) database, the NATMAP 1000 product (Cranfield University, 2012) were used to parameterize JULES simulations. This is a 1 km gridded version of the National Soil Map, where each grid square is attributed with the percentage of the various soil series included in it. As the NSRI dataset has limited information about Chalk soils, the question of identifying an alternative way of parameterising the chalk within JULES needed to be addressed. This was tested at the point scale using data from the Warren Farm field site.
We have provided a brief overview of the progress that has been made in the application of NGS, advanced genotyping, association genetics, and GM in lignocellulosic bioenergycrops, most widely deployed at present in poplar. These molecular techniques will underpin the sustainable intensi ﬁcation of new non-food plants that may in future be grown over extensive tracts of marginal agricultural land. These examples have already provided promising results with higher yielding and more stress-tolerant GM lines reported and large numbers of markers/candidate genes identiﬁed for a wide array of key bioenergy traits including growth, disease tolerance, and feedstock quality. Traditional breeding programmes have yielded signi ﬁcant improvements in bioenergycrops, for example, the doubling of willow biomass yields in the past 30 years  . Now these new advances, driven by molecular genetics, will open the way to the application of marker assisted breeding and GS in second generation biofuels for further, more rapid progress. The improvements made in food crops so far show the pivotal role advanced breeding can play in ensuring the sustainable intensi ﬁcation of second generation biofuels (see Outstanding Questions). How signi ﬁcant the role will be for GM feedstocks is unclear, depending on successful outcomes from rigorous ﬁeld testing as well as governmental approval and broad public acceptance, but genomic strategies for selection and breeding are now a reality and are likely to drive breeding programmes forward in the future, with or without GM deployment. We can be optimistic that the large yield gap in these non-food outbreeding, unimproved crops is a tractable target for several new DNA approaches. In conclusion, the successful pursuit of advanced breeding programmes will be central to the development of high-yielding, sustainable non-food bioenergycrops as nations around the world seek to meet their renewable energy commitments.
2.5 Considerations for implementation, establishment and management of bioenergy buffers
Bioenergy buffers are linear landscape elements whose spatial arrangement on farmlands should be carefully designed (position, length and width) taking into consideration the following features of field margins: 1) soil characteristics (e.g. compaction and poor soil drainage); 2) micro topographic conditions (e.g. zones susceptible to waterlogging, shallow groundwaters, lowlands with high nutrient runoff loads); 3) presence of sub irrigation and drainage systems; 4) the boundary of field margins (Marshall & Moonen, 2002) that may encompass hedge bank, fences, farm track, waterways (e.g. stream, channel, headlands) or natural ecological corridors such as windbreaks, hedge tree, grass or wildflower strips. All these features should be considered to avoid low germinability and soil crusting during the crop establishment phase (Lewandowski et al., 2003; Zimmerman et al., 2013a) and yield losses due to shading by existing natural riparian vegetation. On the other hand, surface and subsurface nutrient loads coming from adjacent fields (feature 2-4) might explain the high biomass yields observed in poplar and willow buffers respectively by Fortier et al. (2013b) and Ferrarini et al. (2016). However, the relevance of the hypothesis that soil N and P trapping mechanisms observed in bioenergy buffers (sections 22.214.171.124 and 126.96.36.199) can be considered a valuable natural fertilization has to be fully tested yet. Another potential benefit on yields, especially for willow buffers, might be the presence of a shallow groundwater (Jackson & Attwood, 1996).
Usual Planting and Harvesting Dates Agricultural Statistics Board
December 1997 NASS, USDA
Corn was the leading U.S. crop in 1996, both in terms of value of production and acreage grown. Over 73 million acres of corn were harvested for grain. The acreage for grain comprised 92% of all corn planted. More than 80% of the corn-for-grain acreage lies in the Corn Belt States, with Iowa leading all States and Illinois ranking second. The largest acreage of record, 111 million acres, was harvested in 1917. Acreage decreased from the turn of the century, with the exception of wartime plantings, until the late-1960's. Since then, harvested acreage levels have generally ranged between 60-75 million acres.
When driven by observation-based climatology, JULES is able to represent the large-scale distribution of circumpolar permafrost reasonably well. The model simulates permafrost where it is known to occur and captures more than 95 % of the continuous and discontinuous (more than 50 % spatial coverage) permafrost. However, the total extent appears to be overestimated as JULES also simulates permafrost in ar- eas where the spatial coverage is sporadic (less than 50 %) or even in isolated patches only. Consistent with this we find a general cold bias in the simulated soil temperatures when compared with observations, especially in winter. This may partly be the result of biases in the model forcing data. Un- certainties in the precipitation input affect the simulation of soil temperatures in two ways: by affecting the thickness of the snowpack and therefore the amount of thermal insulation in winter; and by changing the amount of water in the soil which, because of the energy required for phase changes, af- fects the thermodynamics of soil layers close to the freezing point of water. In turn this affects the simulation of thaw depth and ALT. Generally speaking the model appears to overestimate the annual maximum thaw depth, which can at least partly be explained by the relatively coarse vertical resolution in its standard setup, with the bottom layer span- ning over 2 m. However, uncertainties in the observations are large as ALT is highly variable over small scales.