Article (Draft) (Refereed)
Tramberend, Sylvia and Fischer, Günther and Bruckner, Martin and van Velthuizen, Harrij (2019) OurCommonCropland: QuantifyingGlobalAgriculturalLandUsefrom a ConsumptionPerspective. Ecological Economics, 157. pp. 332-341. ISSN 1873-6106
substantial changes considered in these scenarios, mediated by inter- national trade in agricultural commodities. For example, increased agriculturalland requirement would tend to intensify production, with higher rates of inputs used to achieve greater yields. Conversely, if less agriculturalland is needed for food, this may cause a lowering of the production intensity. In both cases, such adaptation in production moderates the landuse consequences, but alters the resource require- ments for other inputs, e.g. fertiliser or pesticide use (Hertel et al., 2016; P. Smith, 2013). However, the results do characterise the demands placed on agricultural production, which can be interpreted as implying an increase in agricultural areas, an equivalent increase in productive e ﬃ ciency (perhaps through greater inputs, i.e. higher intensity), or some combination of the two. Nonetheless, comparison with previous more complex model results suggests that the outcomes here are broadly equivalent. For example the vegan and vegetarian diets in Erb et al. (2016) have a central value for cropland area of approximately 1200 and 1000 Mha, respectively, compared to the low meat diet used here (based on the average diet in India) of 1022 Mha. As expected, for the reasons given above, changes in intensity con- sidered in Erb et al. (2016) but not here appear to moderate the landuse outcomes, i.e. for less agriculturalland to be relinquished, but coupled with a decrease in intensity of production. Therefore, although the adopted approach neglects aspects that would allow robust spatial or temporal predictions of landuse, it does provide a consistent methodology across scenarios allowing comparisons between them, a primary aim of the study.
Agriculture is the most important anthropogenic activity responsible for terrestrial biotic resource extraction, producing 2121.6 million tons of grains, 391.6 million tons of oilseed and 120.5 million tons of cotton globally in 2008 (USDA, 2009). Agricultural practices have identified as the prime contributors of eco toxic impacts. Cotton, rice, and wheat are on principal role around the world. Developed countries use a huge amount of energy, water, and land for food production that emit a vast amount of GHG. However, developing nations like Bangladesh exhaust or deplete the natural resources during agricultural activities instead of more amount of GHG emission. Water use is an important environmental pressure in various parts of the world. Agriculture is by far the most important use: over 70% of the global freshwater consumption is used in that sector (Hockstra and Chapagain, 2008; Kochler, 2009). It is noted that toxic elements are generated as by-products in times of agricultural practices that pollutes the water of surroundings. Agriculture is also the most important user of land. According to the FAO database, about 38% of the total world‟s land area is used for agriculture in 2007 (FAOSTAT, 2010). Eutrophication is associated mostly with food production and consumption due to excessive use of chemical fertilizers, pesticides, herbicides and animal manure.
The downscaling and spatial distribution of NCE disaggregates biogenic crop ﬂuxes such that carbon uptake and release can be identi ﬁed at a subprovince scale and subsequently at the 0.05° resolution (Figure 12). The geospatial estimate of ﬂuxes would not be possible without the use of satellite-based land cover data and spatial distribution methods. The spatial distribution indicates the drawdown of carbon in global crop-intensive regions (e.g., U.S. Corn Belt region) but also illustrates net releases in some grid cells and regions west of the U.S. Corn Belt. The uptake and release of agricultural carbon, presented in units of g C m 2 yr 1 (Figure S2), are similar in pattern to that of Ciais et al. , although the uptake and release are more pronounced in our results. For example, the U.S. Midwest indicates uptake closer to 150 to 200 g m 2 yr 1 in dense crop production regions, as opposed to 100 g m 2 yr 1 estimated by Ciais et al. . There are also regions of high carbon release from livestock (i.e., >100 g m 2 yr 1 ), including the western U.S. grazing lands just west of the dense cropland areas. While the higher uptake and release esti- mates per grid cell may be an artifact of spatial resolution and may counter each other when summed to a coarser scale (i.e., 1° × 1°), the global and regional totals for NPP and harvested carbon remain higher in this study compared to other studies (Table 3).
Impacts on the GHG balance of bioenergy projects can result from both direct and indirect changes in the use and management of agriculturalland and forests. The volume of indirect GHG emissions depends partly on the prior condition of the converted land and the crops historically grown on it. GHG emissions resulting fromlanduse change (LUC) can be offset by using the biomass to displace fossil fuels and improving the uptake of carbon into soils and above-ground biomass. However, if the growing of energy crops displaces existing crop production, which then moves to other regions, particularly if this encourages deforestation, such landuse change may take decades before overall net savings are achieved, if ever (Searchinger et al., 2008). Recovery of biodiversity may take centuries (e.g. Sala et al. 2009). Increased fluxes of nitrous oxide to the atmosphere from increased use of nitrogen fertilizer in crop production can add to the total GHG emissions and may turn the balance to become unfavorable (Crutzen et al. 2008, Howarth et al. 2009). Depending on the future developments of energy cropping systems, crop yield improvements, global food demand and the needs for cropland expansion, sustainable biomass production could make a greater contribution to the future global energy demand than at present, though to what degree is uncertain (SRREN 2011b). Assessing the net GHG effects of growing energy crops requires measurements of LUC impacts and the attribution of any resulting GHGs between co- products. The GHG emissions can vary with the specific situation and are often based upon several causes. A full assessment of the landuse change including indirect effects, however, requires the consideration of landuse for all agricultural or forestry products (see section 5.1). As growing demand for food will already lead to an expansion of globalcropland (see sections 3.1-4), further production of fuel crops will enhance the impacts of landuse change (see Chapter 2).
In this paper biofuel policies are modeled as mandatory blending obligations fixing the share of biofuels in transport fuel. It should be mentioned that this mandatory blending is budget neutral from a government point of view. To achieve this in a CGE model two policies were implemented. First, the biofuel share of transport fuel is specified and made exogenous such that it can be set at a certain target. A subsidy on biofuel inputs is specified endogenously to achieve the necessary biofuel share. The input subsidy is needed to change the relative price ratio between biofuels and crude oil. If the biofuel share is lower than the target, a subsidy on biofuels is introduced to make them more competitive. Second, to implement this incentive instrument as a ‘budget-neutral’ instrument, it is counter-financed by an end user tax on petrol consumption. The end user tax on petrol is made endogenous to generate the necessary budget to finance the subsidy on biofuel inputs necessary to fulfill the mandatory blending. Due to the end user tax, consumers pay for the mandatory blending as end user prices of blended petrol increase. The higher price results from the use of more expensive biofuel inputs relative to crude oil in the production of fuel.
A b s t r a c t . As from 1956 to 1991, agriculturalland and farming practices in Malta have undergone various changes. There has been a 42% decline in agriculturalland area. Fragmentation is a dominant feature with about 13,000 holdings still prevailing. Whole-time farmers have decreased by 80% to 1,473 whilst part-time farmers have increased by 42% to 13,807. Over two million kilos of fertilizer, which are predominantly nitrogenous, together with considerable amounts of pesticides are now being imported. The surge in livestock production and the inadequacy of the system to cater for effluent disposal are further adding pressure to the limited land resource.
As, in agricultural areas, land-use decisions are of private kind, an usual as- sumption is to consider that these decisions aim at maximizing individual gross returns, without accounting for environmental externalities. In our model, risk- neutral farmers are assumed to maximize their expected gross returns, at field level, by choosing between two potential land uses: cropland or grassland. For a given field, the expected gross return depends on two drivers: the economic con- text, which is the same for all fields and will be a matter of discussion later, and the agricultural quality of the field, which is heterogeneous through space and thus between fields. The higher this quality, the higher the crop yield and the higher the likelihood of using this field as a cropland in a given economic context. In an incomplete information framework, we assume that farmers know these qualities, but that the decisionmaker does not. At the regional scale, the sum of individual land-use choices generates a landscape in which a biological population evolves. The biological population is depicted with a metapopulation model where sub- populations, which growth rates depend on the local landuse, are connected by dispersal processes. Grasslands are favorable to the population dynamics, while croplands are not. The population dynamics is spatially explicit, taking into ac- count density dependence of birth-death processes and dispersal.
Soil carbon storage and nutrient cycling as climate services are being increasingly recognised e.g. under UNFCCC as part of national reporting and accounting, as part of life-cycle
greenhouse gas assessments for biofuels, in various regional initiatives and national efforts. The UNFCCC is an international treaty, which came into force in 1994, setting an overall framework for intergovernmental efforts to tackle the challenge posed by climate change. The requirements for the 196 country Signatories (or ‘Parties’) to the UNFCCC include adopting national mitigation policies and publishing national inventories of anthropogenic emissions and sinks of greenhouse gases including activities on the land such as afforestation, deforestation, agricultural management and wetland drainage and rewetting. Developed country signatories have legally binding targets under the Kyoto Protocol and can count land based emissions or sinks towards meeting these targets, thus incentivising activities that protect soil carbon. Developing countries currently have voluntary targets and several countries have made pledges that include reduced deforestation (e.g. Brazil and Indonesia) or afforestation (e.g. 400000 km 2 in China). Under the Clean Development Mechanism (CDM) developed countries can fund projects in developing countries that generate certified emission reduction credits (CERCs). China, for example, has the largest number of CERCs in the world (IFPRI, 2011). Brazil also has 180 CDM projects, the third largest number of CERCs after China and India (Cole & Liverman, 2011). A number of projects in Africa, North America and South Asia have a significant component for soil greenhouse gas emission reduction of soil carbon sequestration, financed through the Verified Carbon Standard or the American Carbon Registry.
Coding is central to inductive analysis (Thomas, 2006). Tasks typically associated with coding include sampling, identifying themes, building codebooks, marking texts, constructing models (relationships among codes) and testing these models against empirical data (Ryan & Bernard, 2000, pp. 780-782). More specifically, the process of coding consists of collecting and analyzing examples of phenomena—or specific statements—in order to find commonalities, differences, patterns and structures (Basit, 2003). These statements are “categorized into clusters of meaning that represent the phenomenon of interest” (Starks & Trinidad, 2007, pg. 1375, citing Creswell, 1977) paying attention to “what was experienced [by the participants] as well as how it was experienced” (ibid., pg. 1376, brackets added) as expressed in the participants’ descriptions. The categories resulting from the coding typically have five basic features (Thomas, 2006, pg. 4): 1) a label for the category; 2) a description of the category; 3) a text or data associated with the category; 4) links; and 5) a type of model in which the category is embedded. Basit (2003) distinguishes two phases in data coding: one focusing on meanings inside the research context and the other concerned with what may be meaningful to outside audiences. Widom (2003) warns against the tendency to “over-code,” as this might result in a “waste of time and money” (pg. 2). To be efficient and avoid over-coding, I tried to stick to “five or six broad topic headings, not thirty” (ibid. pg.1) and, additionally, I always used “a hierarchical node structure” (ibid. pg.4), where “[n]odes are like file folders, or a particular color of highlighter” (ibid.).
The soil quality of the marshland is quite high (Feddersen 1853; InfoNet Umwelt 2007). In the early 19th century crop production was of great importance on Eiderstedt (Hammerich 1984) and the share of arable farm land was quite high. In some years close to half of the agriculturalland was used to grow crops. In the middle of that century cattle farming became the prime means of agricultural production as exports of cattle to the United Kingdom via the harbors of Tönning and Husum were very profitable. Consequently, meadows and grassland with ponds and drainage drills running through became the dominant type of agriculturalland on Eiderstedt. When detailed maps of Germany were drawn up by the Prussian government in the late 1870s, almost 93% of the agriculturalland consisted of grassland (LVermA-SH 2007a). Arable farm land was hardly found (Fig. 1): crop production took place only in the vicinity of the town of Garding and in the Northeast of Eiderstedt.
Land is naturally an important resource in agrarian economies and, thus, efficient land markets (including sales and rentals) are important for sustainable land management and agricultural development. Particularly, land markets allow land to be used by farmers who are more capable to earn the highest return from it, through mobility of scarce factors of production such as labour, draft power, implements, purchased inputs, and management ability. There is an old and large literature on land tenure contracts and their implications for agricultural efficiency. 1 Adam Smith (1776), John Stuart Mill (1848), Alfred Marshall (1890) and numerous authors since have argued that share tenancy causes inefficient resource allocation. The rationale being that the share tenant receives as marginal revenue only a fraction of the value of his marginal product of labor, thus reducing the tenant’s incentive to supply labor or other inputs at the optimal level, assuming that the tenant’s work effort cannot be monitored and enforced and there is no production
Our study indicated a large increase in cropland production in this region, which agrees well with previous observations (e.g., Parton et al., 2007), and the increased productivity had the largest impact on SOC among all factors we have investigated. Enhancement of long-term crop production in the Great Plains can be attributed to increased irrigation, pest management, fertilizer applications, improved tillage practices, and improved plant varieties (Parton et al., 2007). The increase of crop NPP can in turn produce more aboveground residue and root biomass inputs into the soil, resulting in higher levels of SOC (Johnson et al., 2006; Lokupitiya et al., 2012; Wilts et al., 2004). An assessment of European SOC also found that enhanced NPP slowed the loss of SOC and may further increase SOC (Smith et al., 2005). However, some field studies showed NPP increase only had limited impacts on SOC as other factors (e.g., crop rotation) might be changing as well. For example, after reviewing the effects of enhancing crop rotations on the SOC dynamics, West and Post (2008) found changing wheat-fallow rotation to continuous wheat did not increase SOC even though the cropland production increased. In addition, SOC dynamics is confounded by other important factors such as initial SOC level. NPP increase might lead to SOC increase in less fertile regions, as shown in this study and others (Tan and Liu, 2013).
Globalcropland monitoring is important when considering tactical strategies for achieving food sustainability. Different globalland cover (GLC) datasets providing cropland information have already been published and they are used in many applications. The different data input methods, classification techniques, class definitions and production years among the different GLC datasets make them all independently useful sources of information. This study attempted to produce a cropland agreement level (CAL) analysis based on the integration of several cropland datasets to more accurately estimate cropland area distribution. Estimating cropland area and how it has changed on a national level was done by converting the level of cropland agreement into percentages with an existing cropland fraction map. A pre-analysis showed that the four GLC datasets used in the 2005 and 2010 groups had similar year input data acquisitions. Therefore, we placed these four datasets (GlobCover, MODIS LC, GLCNMO and ESACCI LC) into 2005 and 2010 year-groups and selected them to process dataset integration through a CRISP approach. The results of this process proposed four agreement levels for this CAL analysis, and the model correlation was converted into percentage values. The cropland estimate results from the CAL analysis were observed along with FAO data statistics and showed the highest accuracy, with a 0.70 and 0.71 regression value for 2005 and 2010 respectively. In the cropland area change analysis, this CAL change analysis had the highest level of accuracy when describing the total size of cropland area change from 2005 and 2010 when compared to other individual original GLC datasets.
crop mask and has been taken as evidence of the spatial consistency of the final product. Compared to Globcover, the figures of the MARS-JRC and IIASA products seem more consistent with FAOSTAT since they are more concentrated in the vicinity of the identity line. Here, it is worth noting that high correlation was expected for the IIASA product because it has been calibrated with FAO statistics. Consequently, the obtained similar agreement between the MARS-JRC and FAOSTAT, although it was not an explicit objective of the product, is to be considered as additional evidence of the consistency of the product proposed here. The MARS-JRC crop mask tends to over-estimate the cropland areas for the majority of the countries (39 over 47). As the over-estimation appears to be systematic in the logarithmic scale, this means that the over-estimation is in percentage of the cropped area of the countries. The observed over-estimation can be explained by three factors. (i) For three datasets used in the crop mask (Globcover, CUI, and MODIS-JRC), there were mixed cropland classes including 50% to 70% of cropland. For these classes, it is thus possible to have an over-estimation of 43% to 100%. (ii) Shifting cultivation is a common practice in Africa and fallow areas are often counted as cropland areas when visually interpreting high resolution images. The impact of this shifting cultivation on cropland extent is probably higher in equatorial areas than in arid and semi-arid areas where fields are cropped for longer periods. (iii) Finally, the coarse spatial resolution of some datasets (250 m to 1 km) adds to the possible confusion between cropland and natural vegetation at this scale and of the generation of mixed classes.
The purpose of this study was to prepare a cropland suitability map of Mon- golia based on comprehensive landscape principles, including topography, soil properties, vegetation, climate and socio-economic factors. The primary goal was to create a more accurate map to estimate vegetation criteria (above ground biomass AGB), soil organic matter, soil texture, and the hydrothermal coefficient using Landsat 8 satellite imagery. The analysis used Landsat 8 im- agery from the 2016 summer season with a resolution of 30 meters, time series MODIS vegetation products (MOD13, MOD15, MOD17) averaged over 16 days from June to August 2000-2016, an SRTM DEM with a resolution of 30 meters, and a field survey of measured biomass and soil data. In total, 6 main factors were classified and quality evaluation criteria were developed for 17 criteria, each with 5 levels. In this research the spatial MCDM (multi-criteria decision-making) method and AHP based GIS were applied. This was devel- oped for each criteria layer’s value by multiplying parameters for each factor obtained from the pair comparison matrix by the weight addition, and by the suitable evaluation of several criteria factors affecting cropland. General accu- racy was 88%, while PLS and RF regressions were 82.3% and 92.8%, respec- tively.
Agriculture activities also contribute to land degradation through soil erosion from an agriculturalland to streams which reduced the fertility of the soil as most nutrients and organic matter are contained at the topsoil (Sharma et al., 2004). Study of soil loss due to surface runoff is very important to determine erosion hotspot areas which are very widespread in humid tropical regions such as Malaysia (Toum et al., 2005). In an attempt to restore the soil to its original composition, more fertilizers and organic matter must be added. Soil erosion refers to the process where soil particles are removed from earth surface by natural process which will later be transported by wind or water to different place to be deposited. Erosion is the largest portion of NPS pollution in the tropical region as it causes sedimentation in lakes and reservoir, increase flood frequency and reduces storage capacity of lake. Sediment refers to eroded soil or suspended solids due to erosion process or surface runoff on an agriculturalland, stream banks and highly disturbed area.
Finally, other local factors can influence afforesta- tion patterns. For instance, it has been observed that Scots pine tends to grow adjacent and parallel to the terrace walls in the inner part of the terrace, whereas the outer parts are only densely covered by grass. Ter- races modify hydrological functions in the catchment and consequently change soil moisture patterns. As a result, the inner parts of the terraces are more fre- quently saturated, whereas the outer parts retain less water (Gallart et al 1994). This higher water deficit in the outer part of the terraces can enhance competition from well-established permanent pastures in a nutrient- rich environment (because of fertilization during previ- ous agriculturaluse) (Davis et al 1998). Thus, well- established grass cover can pose serious difficulties for tree growth, especially during characteristic Mediter- ranean annual summer droughts, when P. sylvestris can be highly sensitive to soil water deficits (Martínez-Vilal- ta and Piñol 2002). However, even without this drought effect, inhibition of seedling establishment by compact layers of herbaceous vegetation has been reported as an important factor in regulating afforestation processes in secondary meadows in central Europe (Prach et al 1996).
2000). The SD definition of a “policy” differs from the traditional use of the term. Traditional uses of the term “policy” refers to the proposal or adoption of a specific action, whereas the use of the term “policy” in SD refers to the evaluation of an action or other changes to model inputs and their influence on the outcome(s) over time (Sterman, 2000). Additionally, all SD policies result in direct effects on outcomes in a system compared to the traditional indirect and direct influences from policies on outcomes (Sterman, 2000). Direct effects of SD policies may reveal unintended (i.e., unexpected) outcomes or consequences. Because SD policy changes in one system often have unintended impacts on other model inputs, finding an optimal long-term solution that satisfies all stakeholders and their respective concerns may be difficult (Turner et al., 2016a). Predicting long-term impacts of several policies over the same time period and comparing the outcomes of those policies may inform the decision-making process by providing information on the possible intended and unintended outcomes of various proposed solutions (Barlas, 2007; Horschig, Adams, Gawel, Thrän, 2017; Phan et al., 2018; Turner et al., 2013). Therefore, SD policy evaluation techniques may be useful to assess environmental consequences of grassland conversion to cropland resulting from potential changes in regulatory, economic, and social policies, especially in areas with large amounts of grassland where changes may be more notable (Borrelli et al., 2017; Carbutt et al., 2017; Foley et al., 2005).
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