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The increase in soybeanproduction as a source of protein and oil is being stimulated by the growing demand for livestock feed, food and numerous other applications. Significant greenhousegas (GHG) emissions can result from landusechange due to the expansion and cultivation of soybean. However, this is complex to assess and the results can vary widely. The main goal of this article is to investigate the life-cycle GHG balance for soybean produced in Latin America, assessing the implications of direct landusechange emissions and differentcultivationsystems. A life-cycle model, including inventories for soybean produced in three different climate regions, was developed, addressing landusechange, cultivation and transport to Europe. A comprehensive evaluation of alternative landusechange scenarios (conversion of tropical forest, forest plantations, perennial crop plantations, savannah and
Ficus spp.). Soils within the kraal are covered with cattle
dung mixed with their urine.
At the Legon site, the cultivated maize ﬁeld was har- vested prior to the sampling campaign. The ﬁeld has been continuously under maize cultivation for more than ﬁve decades. Weeding is done by hand, and dead weeds and stovers from previous maize crops are left on the soil surface. The 20-year-old Leucaena leucocephala woodlot was adja- cent to the cultivated maize ﬁeld. Originally, this site was cultivated before its conversion to a woodlot for the pro- duction of fuelwood. The soil surface was covered with a thin layer of leaf litter. The natural forest was over 60 years old and consisted of plant species such as Zanthoxylum
Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E.A., Haberl, H., Harper, R., House, J., Jafari, M., Masera, O., Mbow, C., Ravindranath, N.H., Rice, C. W., Robledo Abad, C., Romanovskaya, A., Sperling, F., Tubiello, F., 2014. Agriculture, Forestry and Other LandUse (AFOLU). In: Edenhofer, O., Pichs- Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., Zwickel, T., Minx, J. (Eds.), Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 811– 922 .
4.1.3 Results comparison on distribution of response to the shock
Figure 16: Overview of distribution of effects between demand side and supply side adjustments for all crops modelled in GLOBIOM (aggregated by ton dry matter) and for all scenarios
Figure 16 provides an overview of how the agricultural system reacts to the shocks from the different scenarios. The demand side reacts mostly on feed, because food demand is more inelastic than feed demand, especially for cereals. In the case of oilseeds, feed response is lower and sometimes even negative (soybean), because the yield of protein meals per unit of fuel is stronger than the yield of DDGS for cereals based ethanol and boosts the consumption of other feedstuffs. Co-products themselves are accounted for in a different category (yellow bars) and are directly related to the technical coefficients in the crushing or biofuel supply chain. In many cases, yield is found to be an important contributor. However, some crops do not respond in yield in the model response, in particular sugar crops, because marginal yields are found to be lower than average yields for these crops. For cereal straw, yield response corresponds to use of unsustainable removal rates where straw was previously harvested within sustainable rate limits. Perennials and short rotation crops mainly provide the extra production though area increase, because only one management type is considered for these in the model.
Life Cycle Assessment  is an internationally known methodology for the evaluation of the environmental performance of a product, process or pathway along its partial or whole life cycle, considering the impacts generated from ‘‘cradle to grave”. Biofuel life cycles are often assessed from “cradle to gate”. Several authors   have noted that the LCA of palm oil industries often led to diverging results due to different approaches and methodologies, especially in the case of biofuels. An assessment focusing on the mere energy balance of biodiesel production from palm oil in Thailand was carried out to provide reliable information for promotion decisions . The energy balance of palm oil biodiesel produced in Colombia and Brazil was
Henry et al. (2009) addressed the impact of soybean expansion in agricultural cropland competition. They suggest that soybean expansion accounted for the shift from dairy farms and cattle breeding, the shift from annual crops production (mainly wheat, corn, cotton, sunflower, sorghum and rice) and the decrease in pastures rotation. The displaced activity, however, varies geographically, mainly between the traditional soybeancultivation area (Santa Fe, Cordoba, Buenos Aires and Entre Rios) and the marginal area (Santiago del Estero, Salta, Chaco, and Tucuman) (Grau et al. 2005; Henry et al. 2009). OEA (2009) compared National Agricultural Census data between 1988 and 2002 to explore soybeanproduction patterns among different regions. In the Central region, dairy farms have been replaced by soybean plantations. Moreover, Coutinho et al. (2008) indicates that rotations with pastures have been reduced and displaced to the Northern part of Santa Fe. In Cordoba the same process has occurred mainly replacing pastures in the Pampas. In Santa Fe, 70% of the agricultural area is soybean, where soybean area has almost doubled the expansion of the agricultural area, suggesting that other activities have been substituted. According to OEA (2009) 60% of the soybean area expansion was done by first occupation, suggesting that crops rotation has been reduced. They state that soybean has replaced pastures, mainly perennial and other oilseeds, particularly sunflower.
Recent regulations on biofuels require reporting of greenhousegas (GHG) emission reductions related to feedstock-speciﬁc biofuels. However, the inclusion of GHG emissions from land-usechange (LUC) into law and policy remains a subject of active discussion, with LUC – GHG emissions an issue of intense research. This article identiﬁes key modelling choices for assessing the impact of biofuel production on LUC–GHG emissions. The identiﬁcation of these modelling choices derives from evaluation and critical comparison of models from commonly accepted biofuels – LUC – GHG modelling approaches. The selection and comparison of models were intended to cover factors related to production of agricultural-based biofuel, provision of land for feedstock, and GHG emissions from land-use conversion. However, some fundamental modelling issues are common to all stages of assessment and require resolution, including choice of scale and spatial coverage, approach to accounting for time, and level of aggregation. It is argued here that signiﬁcant improvements have been made to address LUC–GHG emissions from biofuels. Several models have been created, adapted, coupled, and integrated, but room for improvement remains in representing LUC–GHG emissions from speciﬁc biofuel production pathways, as follows: more detailed and integrated modelling of biofuel supply chains; more complete modelling of policy frameworks, accounting for forest dynamics and other drivers of LUC; more heterogeneous modelling of spatial patterns of LUC and associated GHG emissions; and clearer procedures for accounting for the time-dependency of variables. It is concluded that coupling the results of different models is a convenient strategy for addressing effects with different time and space scales. In contrast, model integration requires uniﬁed scales and time approaches to provide generalised representations of the system. Guidelines for estimating and reporting LUC – GHG emissions are required to help modellers to deﬁne the most suitable approaches and policy makers to better understand the complex impacts of agricultural-based biofuel production.
Table 2 and Figure 5 show the impact of livestock output growth on landuse. The figures represent the differ- ence in landuse between the first and second experiments explained above. As is evident in the table and the figure, with the increase in livestock output, pasture area is expanded while forest land is reduced in all regions. However, the overall change in crop land has a mixed outcome. In most of the regions, cropland is reduced, while in regions such as Canada (CAN), CHIHKG, and Mala_Indo cropland expanded. Globally there is a net increase of about 44.5 billion hectares of pasture, with a decrease in cropland and forest of 1.1 billion and 43.3 billion hectares respectively (Table 2). Among the regions, CHIHKG emerges as the region of largest pasture expansion/deforestation followed by S_S_AFR, BRAZIL, South and Other Americas (S_o_Amer), and R_S_ Asia (Table 2). The expansion of pasture is small in the advanced economies such as the EU27, CAN, JAPAN, Oth_Europe, and Oceania.
There was decreased light penetration at both the UNFA and UNFB sites, but Ritter et al. (2005) found that the rapid succession in new forests keeps soil temperatures similar to that of a mature natural forest. This explains why the soil temperatures were still very similar at the RH and UNFA/B sites. The UNFA site was characteristic of waterlogged soils, and coupled with slow decomposition of organic matter also led to lower soil temperatures (Rayment and Jarvis 2000). Oelbermann and Raimbault (2015) found that there was increased litterfall and soil organic matter (SOM) at the UNFA site compared to the RH site, and along with the saturated conditions at the UNFA site, this would explain the lower soil temperatures. The lack of waterlogged conditions at the RH and UNFB sites also explains why they have consistently higher soil temperatures than the UNFA site, as they had similar soil moisture contents despite differences in SOM. All land-uses experienced significantly higher soil temperatures in the summer compared to both spring and fall. James et al. (2003) observed this same trend between a riparian grassland, shrubland and forest in Saskatchewan, Canada. This is likely due to the higher ambient air temperatures, as well as increased PPFD (Smith et al. 2003; Jurik and Van 2004).
Freshwater ecosystems can be important sources of greenhouse gases (GHGs) to the atmosphere due to their ability to actively process and transform terrestrial inputs of organic matter and other solutes (Battin et al., 2009; Cole et al., 2007). Although the terrestrial landscape is generally considered an important carbon (C) sink, with recent estimates suggesting a net uptake of 3.6 Pg C per year (Keenan & Williams, 2018), streams and rivers may provide sufficient emissions of GHGs to offset the terrestrial C sink. Streams are frequently
In the CAPRI LCA module (Weiss and Leip 2012 , Leip et al 2014b , Westhoek et al 2015 ), total agri- cultural emissions E agri were estimated as the sum of
ﬂows caused by agricultural production activities, plus emissions caused in earlier phases of the products life cycle, including energy use or landusechange. Supple- mentary Information S1 gives detailed results of the N-LCA for the main six vegetable and six livestock product groups to which the data were aggregated. Differently from Weiss and Leip ( 2012 ) and in accor- dance to Leip et al ( 2014b ) the allocation of ﬂows from primary crop products to secondary products (e.g. soya to soybean oil and soybean cakes) is done by mass. The allocation of emissions from feed produc- tion to speci ﬁc livestock products makes use of the animal budget module in CAPRI where energy and protein requirements are matched with domestic and imported feed supply, and data on farm expenditures for feed (Britz and Witzke 2012 , Leip et al 2011d ). In a ﬁrst step, emissions from crop activities are converted into emission intensities and allocated to animal activ- ities and in a second step to animal products (Weiss and Leip 2012 ).
For most natural environments such as soils, it is known that quantitatively soil properties within a site on the land- scape are relatively similar. It is noted that spatial char- acterization of soil properties is necessary in order to lo- cate homogenous areas to be carefully managed for agri- cultural sustainable development [13-15]. In this regard, the major problems are how to identify some of the fac- tors which influence variations in soil properties and use this knowledge to design agricultural management prac- tices that would be both environment friendly and highly productive. Thus, full characterization of soils requires the exploration of soil properties at different depths for proper management of water and nutrient in the root zone and in a broader perspective, for modelling of environ- mental processes. This could provide relevant informa- tion on patterns of nutrient accumulation and redistribu- tion at both surface and deeper layers of the soil, as well as the rate of net losses.
We combine data on global distribution of biodiversity with data on rapidly expanding land-based renewable energies to identify areas of conflict between biodiver- sity and energy development. We show that global key areas for biodiversity protection may be under threat from increasing renewable energy development in the near future. The magnitude of risk is dependent on the type of RE source harvested, the restrictions imposed on energy harvest and the region considered, with Cen- tral America appearing at particularly high potential risk from RE development. When no restrictions on the extraction of RE apply, we identify a major potential threat to biodiversity from bioenergy cultivation, while the potential impact of wind energy and solar PV appears comparatively lower. However, these differ- ences are reduced when energy potential is restricted by external factors, in particular by local energy demand. Overall, we found that areas of opportunity for developing solar PV and wind energy with little harm to biodiversity could exist in several regions of the world, although without conversion of large land areas and long-scale power transmission, the contribu- tion to satisfying existing demand is very low. In con- trast, areas of opportunity for bioenergy production in land with low priority for biodiversity protection are scarce, irrespective of any additional external factors restricting energy production potential. This result arises from the fact that productive land in the tropical regions is usually good for biodiversity as well as for bioenergy generation (Gaston, 2000; Koh & Wilcove, 2008; Pogson et al., 2013).
Adji F.F., Hamada Y., Darang U., Limin S.H., and Hatano R., 2014. Effect of plant-mediated oxygen supply and drainage on greenhousegas emission from a tropical peatland in Central Kalimantan, Indonesia. Soil Sci. Plant Nutrition, 60, 216-230. https://doi.org/10.1080/00380768.2013.872019 Bond-Lamberty B., and Thomson A., 2010. A global database
Plant growth is closely correlated to source and sink strength and the balance between them (Gifford and Evans, 1981; Wardlaw, 1990; Smith and Stitt, 2007). Source strength is defined as the rate at which assimilates are produced (photosynthesis rate); while sink strength is the competitive ability of an organ to attract assimilates (Marcelis, 1996). The source-sink balance of a plant varies significantly during its life span because of the continuous organ initiation and development which controls the growth of sinks as well as the photosynthetic sources (Wardlaw, 1990). During the early growth stage, tomato plants might be prone to sink limitation as there are not sufficient sinks to utilize all the produced assimilates. This might occur particularly at ample irradiance. During the reproductive stage, tomato plants generally bear high fruit load, and assimilate supply might not meet the sink demand. This has been proven in studies where fruit pruning increased fruit size of the remaining fruits without influencing the total plant biomass production (Cockshull and Ho, 1995; Heuvelink, 1996b; Matsuda et al., 2011), which suggests source limitation. Tomato source-sink balance could also differ between cultivars which often differ in fruit load and potential fruit growth rate which is a measure for sink strength (Heuvelink and Marcelis, 1989; Marcelis, 1996). Cultivars may also differ in source strength as leaf photosynthetic properties, leaf area and plant architecture may differ. Dueck et al. (2010) observed that under commercial crop management cherry tomato did not benefit that much from the use of artificial lighting compared with the cultivars with large-sized fruits, and they argued that cherry tomato had less sink demand although it has a higher number of fruit load. A detailed analysis of the source-sink balance from early growth stage to fully fruiting stage for cultivars with different potential fruit size has not been done so far. Crop growth models have been applied to quantify the source and sink strength (Heuvelink, 1996b; Wubs et al., 2009; Wubs et al., 2012). The sink strength of a growing organ can be determined by its potential growth rate (i.e. growth under non-limiting assimilate supply), and depends on its developmental stage (Marcelis and Baan Hofman-Eijer, 1995); integrating the sink strength of each growing organ over the whole plant results in total plant sink strength. The source strength is the supply of assimilates, which can be determined by integrating the leaf photosynthesis over the crop canopy (Heuvelink, 1995).
New England states have seen a downturn in secondary forest recovery since 1970 . CT has witnessed an increasing amount of undeveloped forest land being converted to development from 1985 to 2010 . Land development encompasses not just residential and commercial building as well as roads, subdivisions and other incursive forms of urban and suburban landuse. Land conversion in CT has been piecemeal, incremental and often decentralized at the level of township or property owner, masking the long-term regional impact of devel- opment and deforestation. Forest conversion to low-density housing (6 - 25 homes/km 2 ) was considered the fastest growing driver of New England’s land cover change over this period . In-state population growth is a key indicator value when gauging the efficiency of Connecticut’s residential land development. US Census data reveals a highly skewed growth ratio of population growth in CT to developed landuse, using a low-threshold housing development density of at least one housing unit per four acres to plot land development by census tract. In percentage terms, rates of land development grew at roughly eight times the population increase over the pe- riod 1970-2000  (Figure 1). As a point of contrast, the Pacific Coastal Region forecasts forest cover reduc- tion of four percent for the fifty-year period of 1997 to 2050 . Over the next decades, CT’s ratio of forest lost to population growth is expected to exceed Washington’s by six-to-one, without accounting for future de- mographic shifts west.
predominant natural land cover in the Piedmont ecoregion of North Carolina is oak-hickory- pine forests (Griffith et al., 2002a; Griffith et al., 2002b). Forest soils allow precipitation that is not used in transpiration, to infiltrate to the groundwater, thereby sustaining the base flow in surface water systems and recharging groundwater (Booth, 1991). Forested riparian areas stabilize stream banks, keep water cool by providing shade, and contribute wood and leaf litter for habitat, cover, and food for aquatic organisms (Dolloff and Melvin, 2003; Quinn et al., 2001). Forested wetlands have the added benefit of trapping contaminants and sediment rather than letting them wash downstream (Johnston, 1991).
Contrary to the previous findings that landuse intensity of in-situ projects are lower than surface mning (Jordaan, Keith, and Stelfox 2009), we found that in-situ projects have significantly higher weighted average LUI of 3.6 m 2 /m 3 compared to 0.58 m 2 /m 3 for surface mining (Figures 4 and 5). Three in-situ projects in the study area (ConocoPhillips Canada-Surmont, Nexen-Long Lake and MEG Energy Corp.-Christina Lake Regional), have the highest LUI observed. The reasons for this disturbance vary. Long Lake has a central processing facility (CPF) in the north, as well as significant disturbance (associated with seismic features, well activity and drainage areas) identified in the Kinosis SAGD area in the south between 2004 and 2009 (Nexen 2011). In contrast, Surmont site disturbance is caused largely not by its CPF but by the extensiveness of disturbance across the landscape in the form of road infrastructure, well pads, and the forest areas replaced by bare soil. These areas correlate closely with identifiable seismic features and drainage areas in the company's Annual Performance Review (ConocoPhilips 2013).
18.104.22.168. Crop residue management. Average yields for various crops were calculated from Abstracts of Agriculture ( DAFF,
2013a , 2015 ). Yields were either obtained or calculated using historical statistics of productions and area planted in the country. Residue to yield ratios were obtained from IPCC (2006) , Unal and Alibas (2007) , Scarlat et al. (2010 , 2011 ) and Jain et al. (2006 , 2014) . Based on yields, crop residue management and crop factors ( Tables 3 and 4 ), quantities of crop residues that were retained in the fields after harvest were then calculated by Agriculture and LandUse National GreenhouseGas Inventory Software (ALU) using the information that was obtained during the survey. Area under production in 2012 was obtained from Department of Agriculture, Forestry and Fisheries ( DAFF, 2013b ) and Statistics South Africa (2007) . 22.214.171.124. Liming. Lime is commonly applied to agricultural lands where nitrogenous fertilisers are continuously used and where precipitation exceeds evapotranspiration ( Ogle et al., 2014 ). Data on national consumption of lime in the country was not available and had to be estimated from management practices obtained from the survey. Data for this study was es- timated from area planted, average liming rate and frequency of application ( Table 4 ). Since lime is not applied every year, its input rates are evenly distributed on an annual basis ( Rajaniemi et al., 2011 ; Camargo et al., 2013 ). Annual equivalent amount of lime applied was estimated to be a product of area harvested and lime application rate divided by frequency of application. Agricultural lime consists primarily of crushed limestone (CaCO 3 ) and dolomite (CaMg(CO 3 ) 2 ) in varying pro- portions ( Ogle et al., 2014 ). Fractions of dolomite (63%) and limestone (37%) were determined based on Otter et al. (2010) . The total amount of lime (3.5 million tonnes) that was calculated and used in this study exceeded the 1.1 million tonnes for