**Assessing Agricultural Potential in South Sudan – A Spatial Analysis Method**

Xinshen Diao, Liangzhi You, Vida Alpuerto and Renato Folledo

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/47938

## **1. Introduction**

138 Application of Geographic Information Systems

(Spring, 2008), pp.41-51, ISSN 1357-5317

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After almost five decades of war and armed conflict, South Sudan achieved its independence in July 2011. Expectations are high that the independence will bring peace, food security, improved health, and prosperity to its people. The world's newest nation, South Sudan is naturally endowed with agricultural potential given its favourable soil, water, and climatic conditions. It is estimated that about 70 percent of total land area is suitable for producing a wide range of agricultural products, including annual crops such as grains, vegetables, tree crops such as coffee, tea, and fruits, livestock, fishery, and various forest products. To realize such agricultural potential and achieve economic development and broad-based improvements in the nation's living standards, a realistic understanding of the country's initial conditions is required such that appropriate policy measures and agricultural growth strategy can be designed in the near future.

This chapter focuses on analyzing a more realistic agricultural potential in South Sudan in five to ten year horizon. While such analysis seems to be straightforward in most other countries, it is a monumental task in South Sudan given its protracted history of violence. A functional government statistics system that regularly collects socio-economic data literally did not exist during the turmoil years. Hence, our analysis needed to put together different spatial data from several available sources. The key GIS datasets that we used are the 2009 Land Cover data which provides land use information for South Sudan, the Oak Ridge National Laboratory's 2001 LandScan population data, and the most recently updated road condition surveys conducted by World Food Program (WFP). We combine these GIS datasets with the 2008 population census and 2009 National Baseline Household Survey (NBHS) carried out by the country's National Bureau of Statistics (formerly known as Southern Sudan Centre for Census, Statistics and Evaluation). While the agricultural potential is analyzed spatially, the socio-economic datasets, which are both nationally

© 2012 Diao et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Diao et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

representative, allow the statistical analysis to be carried out at subnational levels such as at the state and livelihood zone levels.

Assessing Agricultural Potential in South Sudan – A Spatial Analysis Method 141

significantly underdeveloped in agricultural production. While the large land areas under natural vegetation definitely indicate huge agricultural potential in the country, the challenges to develop them into agricultural land, including required large physical investments and difficulty in identifying suitable farming systems and crop patterns, are

Source: Authors' aggregation using Land Cover database (FAO 2009). **Map 1.** Spatial distribution of aggregated types of land use

We further consider the extent of land use types at both state and livelihood zone levels to understand its distribution (Map 2). In terms of cropland distribution, Western Flood Plains, which covers parts of Northern Bahr el Ghazal, Warrap, Unity and Lakes, is the most important livelihood zone, providing 34.2 percent of national cropland and 24.2 percent of national cropland mixed with grass and trees. Moreover, this zone has the highest ratio of cropland over total land, as cropland and cropland mixed with grasses/trees account for 8.5 and 5.4 percent of zonal territorial area, respectively. Greenbelt (spanning parts of Western

huge.

In the next section, we estimate the size and distribution of the different types of land use, as well as the association between agricultural potential and population density in South Sudan. Based on the agricultural consumption and production patterns, the current agricultural values in monetary terms are calculated in Section 3. In the same section, we then estimate the agricultural potential value in the next five to ten years by simulating an increase in cultivated area though cropland expansion and improvements in agricultural productivity. Section 4 concludes.

## **2. Spatial distribution of different types of land use**

The country's current land use and coverage in the different states1 and livelihood zones2 is described in this section. Then, we use the length of growing period (LGP)3 as proxy for determining typologies of agricultural production potential and describe the relationship between such potential and population density.

## **Current land use**

We use a two-step process to derive South Sudan's land use from almost 300 types based on Land Cover data obtained from FAO in 2009. First, the land use types were resampled and aggregated into 18 classes as depicted in Map 1. In the second step, we further aggregated the land use types into 8 categories (Table 1). For agricultural production potential, we use LGP equal to or more than 180 days as an indicator for sufficient moisture and temperature conditions that permit crop growth. Using this threshold, about 80 percent of the country's territory is under climatic conditions that are considered suitable for agriculture. However, the aggregation of the land use types indicates that most of the land that is suitable for agriculture is still under natural vegetation. As shown in Table 1, land that is currently under crop cultivation, most of which are rainfed, accounts for less than 4 percent of total land. Conversely, the largest part of the country is still under trees and shrubs (62.6 percent). Given the country's favorable agricultural climate condition, this ratio is clearly very low as the crop areas account for more than 28 percent of national land in Kenya and 8 in Uganda. Before South Sudan became an independent country, crop areas in Sudan as a whole accounts for 7 percent of total land. Given that the agro-climate conditions are less favorable in the northern Sudan than that in South Sudan, it is obvious that South Sudan is

1 South Sudan has ten states: Upper Nile, Jonglei, Unity, Warrap, Northern Bahr el Ghazal, Western Bahr el Ghazal, Lakes, Western Equatoria, Central Equatoria, and Eastern Equatoria.

2 The country is divided into seven livelihood zones that are identified under the country's livelihood profile project and defined based on climate conditions and farming systems (SSCCSE, 2006): Eastern Flood Plains, Greenbelt, Hills and Mountains, Ironstone Plateau, Nile-Sobat Rivers, Pastoral, and Western Flood Plains.

3 The concept length of growing period is used in the Global Agro-Ecological Zone Project led by the International Institute for Applied Systems Analysis and the UN Food and Agriculture Organization. For more detailed information, see Fisher et al. (2002).

significantly underdeveloped in agricultural production. While the large land areas under natural vegetation definitely indicate huge agricultural potential in the country, the challenges to develop them into agricultural land, including required large physical investments and difficulty in identifying suitable farming systems and crop patterns, are huge.

Source: Authors' aggregation using Land Cover database (FAO 2009).

**Map 1.** Spatial distribution of aggregated types of land use

140 Application of Geographic Information Systems

the state and livelihood zone levels.

productivity. Section 4 concludes.

**Current land use**

between such potential and population density.

Lakes, Western Equatoria, Central Equatoria, and Eastern Equatoria.

information, see Fisher et al. (2002).

and Mountains, Ironstone Plateau, Nile-Sobat Rivers, Pastoral, and Western Flood Plains.

**2. Spatial distribution of different types of land use**

representative, allow the statistical analysis to be carried out at subnational levels such as at

In the next section, we estimate the size and distribution of the different types of land use, as well as the association between agricultural potential and population density in South Sudan. Based on the agricultural consumption and production patterns, the current agricultural values in monetary terms are calculated in Section 3. In the same section, we then estimate the agricultural potential value in the next five to ten years by simulating an increase in cultivated area though cropland expansion and improvements in agricultural

The country's current land use and coverage in the different states1 and livelihood zones2 is described in this section. Then, we use the length of growing period (LGP)3 as proxy for determining typologies of agricultural production potential and describe the relationship

We use a two-step process to derive South Sudan's land use from almost 300 types based on Land Cover data obtained from FAO in 2009. First, the land use types were resampled and aggregated into 18 classes as depicted in Map 1. In the second step, we further aggregated the land use types into 8 categories (Table 1). For agricultural production potential, we use LGP equal to or more than 180 days as an indicator for sufficient moisture and temperature conditions that permit crop growth. Using this threshold, about 80 percent of the country's territory is under climatic conditions that are considered suitable for agriculture. However, the aggregation of the land use types indicates that most of the land that is suitable for agriculture is still under natural vegetation. As shown in Table 1, land that is currently under crop cultivation, most of which are rainfed, accounts for less than 4 percent of total land. Conversely, the largest part of the country is still under trees and shrubs (62.6 percent). Given the country's favorable agricultural climate condition, this ratio is clearly very low as the crop areas account for more than 28 percent of national land in Kenya and 8 in Uganda. Before South Sudan became an independent country, crop areas in Sudan as a whole accounts for 7 percent of total land. Given that the agro-climate conditions are less favorable in the northern Sudan than that in South Sudan, it is obvious that South Sudan is

1 South Sudan has ten states: Upper Nile, Jonglei, Unity, Warrap, Northern Bahr el Ghazal, Western Bahr el Ghazal,

2 The country is divided into seven livelihood zones that are identified under the country's livelihood profile project and defined based on climate conditions and farming systems (SSCCSE, 2006): Eastern Flood Plains, Greenbelt, Hills

3 The concept length of growing period is used in the Global Agro-Ecological Zone Project led by the International Institute for Applied Systems Analysis and the UN Food and Agriculture Organization. For more detailed We further consider the extent of land use types at both state and livelihood zone levels to understand its distribution (Map 2). In terms of cropland distribution, Western Flood Plains, which covers parts of Northern Bahr el Ghazal, Warrap, Unity and Lakes, is the most important livelihood zone, providing 34.2 percent of national cropland and 24.2 percent of national cropland mixed with grass and trees. Moreover, this zone has the highest ratio of cropland over total land, as cropland and cropland mixed with grasses/trees account for 8.5 and 5.4 percent of zonal territorial area, respectively. Greenbelt (spanning parts of Western

Equatoria and Central Equatoria) and Eastern Flood Plains (encompassing Upper Nile and parts of Jonglei) are the two other major crop producing regions, accounting for respectively, 17.6 percent and 26.2 percent of national cropland, and 25.7 percent and 14.6 percent of the country's land mixed crops with grasses/trees. Both zones also have high ratio of cropland to total land as lands with crops and crops mixed with grasses/trees account for 11.4 percent of total land in Greenbelt and 6.8 percent of total land in Eastern Flood Plains. In total, these three agricultural zones provide 78 percent of national cropland and 64.6 percent of national cropland mixed with grass/tree, but only covers about 47 percent of national territorial area.

Assessing Agricultural Potential in South Sudan – A Spatial Analysis Method 143

The majority of South Sudanese (85 percent) lives in rural areas, which we classify into two categories: "low density" areas with population less than 10 per square kilometer (10/km2) and "medium to high density" areas with population above that threshold. With 13 people per km2, the average population density is very low in South Sudan compared to other countries in the region. The low average is driven by the fact that only 25 percent of the population lives in 83.4 percent of the total territorial lands in South Sudan (Table 2). Accordingly, the population density averages 4/km2 in these areas. In contrast, the remaining 75 percent of the population resides in "medium to high density" areas representing just 16.6 percent of country's total land, thereby resulting to density of 57/km2.We combine the LGP and population density categories that results in six

Our analysis indicates that Type HH, HL, and MH, which are the three typologies of high agricultural potential areas, collectively cover 54 percent of total crop land. This is mostly driven by large areas of MH in Warrap and Lakes representing 26.7 percent of total cropland area (Map 3). This is followed by Type HH (15.3 percent) which can be attributed to the similarly large areas of high population density-high agricultural potential in Western Equatoria and Central Equatoria. Among crop production zones, Greenbelt has the highest share of cropland distinguished as Type HH, while Western Flood Plains dominates the MH category (Map 3). On the other hand, half of the cropland areas in the Eastern Flood Plains are characterized as LL primarily because of the large contribution of Upper Nile region that

agricultural potential typologies (Table 2; Map 3).

Source: Authors' estimates.

falls under this category.

**Map 2.** The ten states and seven livelihood zones

Source: Authors' aggregation from 2009 Land Cover.

**Table 1.** Area and share of total land, by aggregated types of land use

### **Agricultural potential and population density**

Based on the LGP classification, about 27.3 percent of cropland in South Sudan is located in areas with high agricultural potential (LGP of more than 220 days) and another 41.5 percent in the medium potential areas (LGP between 180 to 220 days) (Table 2). To some extent, population determines the current crop production, as well as fulfilling crop system's potential for intensive farming in the short to medium term. Roughly 34 percent and 46 percent of population lives in such areas of high and medium agricultural potential, respectively.

The majority of South Sudanese (85 percent) lives in rural areas, which we classify into two categories: "low density" areas with population less than 10 per square kilometer (10/km2) and "medium to high density" areas with population above that threshold. With 13 people per km2, the average population density is very low in South Sudan compared to other countries in the region. The low average is driven by the fact that only 25 percent of the population lives in 83.4 percent of the total territorial lands in South Sudan (Table 2). Accordingly, the population density averages 4/km2 in these areas. In contrast, the remaining 75 percent of the population resides in "medium to high density" areas representing just 16.6 percent of country's total land, thereby resulting to density of 57/km2.We combine the LGP and population density categories that results in six agricultural potential typologies (Table 2; Map 3).

Source: Authors' estimates.

142 Application of Geographic Information Systems

national territorial area.

Area Share of

*A: By 18 types of land use categories B: By 8 aggregated categories*

Rainfed crop on post flood land 25.4 0.0 Trees, shrubs and other

**Table 1.** Area and share of total land, by aggregated types of land use

**Agricultural potential and population density**

Rainfed crop 2,379.3 3.7 Cropland 2,477.7 3.8 Irrigated crop 32.1 0.0 Grass with crop 325.1 0.5 Rice on flood land 6.0 0.0 Trees with crop 1,707.3 2.6 Fruit crop 0.1 0.0 Grass 9,633.8 14.9 Tree crop, plantation 6.2 0.0 Shrub and tree 40,526.9 62.6

Rainfed crop on temporary flood land 28.5 0.0 Water and rock 482.7 0.7 Grass with crop 325.1 0.5 Urban 37.0 0.1 Shrub with crop 4.3 0.0 **Total 64,688.3 100.0**

Based on the LGP classification, about 27.3 percent of cropland in South Sudan is located in areas with high agricultural potential (LGP of more than 220 days) and another 41.5 percent in the medium potential areas (LGP between 180 to 220 days) (Table 2). To some extent, population determines the current crop production, as well as fulfilling crop system's potential for intensive farming in the short to medium term. Roughly 34 percent and 46 percent of population lives in such areas of high and medium agricultural potential,

(in 1000

Shrub or tree with crop 1,703.0 2.6 Grass 9,633.8 14.9 Shrubs 20,506.6 31.7 Tree with shrub 17,694.9 27.4 Woodland with shrub 2,325.4 3.6

flood land 9,497.6 14.7 Water 350.1 0.5 Rock 132.6 0.2 Urban 37.0 0.1 **Total 64,688.3 100.0** Source: Authors' aggregation from 2009 Land Cover.

Tree, shrub, and other vegetation on

respectively.

Equatoria and Central Equatoria) and Eastern Flood Plains (encompassing Upper Nile and parts of Jonglei) are the two other major crop producing regions, accounting for respectively, 17.6 percent and 26.2 percent of national cropland, and 25.7 percent and 14.6 percent of the country's land mixed crops with grasses/trees. Both zones also have high ratio of cropland to total land as lands with crops and crops mixed with grasses/trees account for 11.4 percent of total land in Greenbelt and 6.8 percent of total land in Eastern Flood Plains. In total, these three agricultural zones provide 78 percent of national cropland and 64.6 percent of national cropland mixed with grass/tree, but only covers about 47 percent of

total land Area Share of

vegetation on flood land 9,497.6 14.7

ha) (%) (in 1000

total land

ha) (%)

**Map 2.** The ten states and seven livelihood zones

Our analysis indicates that Type HH, HL, and MH, which are the three typologies of high agricultural potential areas, collectively cover 54 percent of total crop land. This is mostly driven by large areas of MH in Warrap and Lakes representing 26.7 percent of total cropland area (Map 3). This is followed by Type HH (15.3 percent) which can be attributed to the similarly large areas of high population density-high agricultural potential in Western Equatoria and Central Equatoria. Among crop production zones, Greenbelt has the highest share of cropland distinguished as Type HH, while Western Flood Plains dominates the MH category (Map 3). On the other hand, half of the cropland areas in the Eastern Flood Plains are characterized as LL primarily because of the large contribution of Upper Nile region that falls under this category.

Assessing Agricultural Potential in South Sudan – A Spatial Analysis Method 145

Population (%) 25.4 33.8 15.8 75.1 Population density 66 54 51 57 Land (%) 4.8 7.8 3.9 16.6 Cropland area (%) 15.3 26.7 17.9 59.9 Cropland ha per capita 0.18 0.23 0.33 0.24

Population (%) 8.7 11.9 4.4 24.9 Population density 3 4 3 4 Land (%) 31.5 35.2 16.7 83.4 Cropland area (%) 12.0 14.9 13.2 40.1 Cropland ha per capita 0.41 0.37 0.89 0.48

Population (%) 34.1 45.7 20.2 100.0 Population density 12 13 12 13 Land (%) 36.4 43.0 20.6 100.0 Cropland area (%) 27.3 41.5 31.1 100.0 Cropland ha per capita 0.24 0.27 0.46 0.30

Total LGP>220 days 180-220 days <180 days

 **Agricultural potential defined by LGP**

High Medium Low

Source: Authors' calculation based on 2001 LandScan and 2009 FAO Land Cover.

**3. Estimating agricultural potential**

**Realized agriculture potential**

total rural household's purchases.

**Table 2.** Cropland, population, and population density according to agricultural potential

Because of the country's diverse agro-ecological conditions, crops produced and consumed often differ spatially. With the absence of official agricultural production statistics in South Sudan and given that the agriculture system in the country is presently dominated by subsistence farming, we use the household food consumption data from the 2009 NBHS to estimate the current spatially disaggregated agricultural production.4 To be able to understand the country's agricultural potential, it is first necessary to derive a consistent measure of the current agricultural value for different locations, which we herein refer to as the "realized agriculture potential". The calculation considers both quantity of consumption

4 With the exception of cereals, we assume that all agricultural products consumed in South Sudan are produced domestically. For these products, total consumption is assumed to equal domestic production; for cereals, we used a multi-step process because the country imports significant amounts of maize from Uganda and sorghum from Sudan. First, we convert cereal flour consumption into grain by assuming that 1 kg of flour is produced from 1.25 kg of raw grain. Second, following the assumption used by FAO/WFP, we approximate that post-harvest losses at 20 percent. Third, it is assumed that 55 percent of grain purchased by rural households is produced locally, while the rest is supplied by imports; for urban households, we assumed that purchases are mainly supported by imports. Finally, domestic grain production is defined as consumption met by households' own production, stocks, and 55 percent of

**Population density**

Low <10/km2

Total

High-medium Population>10/km2

Source: Authors' estimates.

Note: **HH**: High agricultural potential /high-medium population density; **HL**: High agricultural potential/low population density; **MH**: Medium agricultural potential and high-medium population density; **ML**: Medium agricultural potential/low population density; **LH**: Low agricultural potential and high-medium population density; and **LL**: Low agricultural potential and low population density.

**Map 3.** Spatial patterns of agricultural potential and population density

The results also show that the potential for agricultural production and population density are spatially correlated. The areas classified as having "high" and "medium" potential have the highest population density at 66/km2 and 54/km2, respectively (Table 2). Both are greater than the 50/km2 threshold that is often used to identify the possibility for promoting intensive farming system in an area (Boserup1965; 1981). However, some areas in "high" potential Western and Central Equatoria that are parts of the Eastern Flood Plains have population densities that are low (e.g. these areas are Type HL). This indicates the difficulty of developing an intensive smallholder farming system even in areas with high agricultural potential. Moreover, because the cropland area under "high" potential is almost equally split between "medium to high" and "low" population density, the area of cropland at 0.18 hectare/per capita in the highest agricultural potential areas is extremely small compared with the national average of 0.30 ha/per capita. Nonetheless, among the six typologies, the ones that are best positioned to generate high returns from investments are HH, HL, and MH. Given that more than half of the cropland areas fall under these categories, these areas should be prioritized for agricultural development programs.

Source: Authors' calculation based on 2001 LandScan and 2009 FAO Land Cover.

**Table 2.** Cropland, population, and population density according to agricultural potential

## **3. Estimating agricultural potential**

## **Realized agriculture potential**

144 Application of Geographic Information Systems

Source: Authors' estimates.

and **LL**: Low agricultural potential and low population density.

**Map 3.** Spatial patterns of agricultural potential and population density

should be prioritized for agricultural development programs.

Note: **HH**: High agricultural potential /high-medium population density; **HL**: High agricultural potential/low population density; **MH**: Medium agricultural potential and high-medium population density; **ML**: Medium agricultural potential/low population density; **LH**: Low agricultural potential and high-medium population density;

The results also show that the potential for agricultural production and population density are spatially correlated. The areas classified as having "high" and "medium" potential have the highest population density at 66/km2 and 54/km2, respectively (Table 2). Both are greater than the 50/km2 threshold that is often used to identify the possibility for promoting intensive farming system in an area (Boserup1965; 1981). However, some areas in "high" potential Western and Central Equatoria that are parts of the Eastern Flood Plains have population densities that are low (e.g. these areas are Type HL). This indicates the difficulty of developing an intensive smallholder farming system even in areas with high agricultural potential. Moreover, because the cropland area under "high" potential is almost equally split between "medium to high" and "low" population density, the area of cropland at 0.18 hectare/per capita in the highest agricultural potential areas is extremely small compared with the national average of 0.30 ha/per capita. Nonetheless, among the six typologies, the ones that are best positioned to generate high returns from investments are HH, HL, and MH. Given that more than half of the cropland areas fall under these categories, these areas Because of the country's diverse agro-ecological conditions, crops produced and consumed often differ spatially. With the absence of official agricultural production statistics in South Sudan and given that the agriculture system in the country is presently dominated by subsistence farming, we use the household food consumption data from the 2009 NBHS to estimate the current spatially disaggregated agricultural production.4 To be able to understand the country's agricultural potential, it is first necessary to derive a consistent measure of the current agricultural value for different locations, which we herein refer to as the "realized agriculture potential". The calculation considers both quantity of consumption

4 With the exception of cereals, we assume that all agricultural products consumed in South Sudan are produced domestically. For these products, total consumption is assumed to equal domestic production; for cereals, we used a multi-step process because the country imports significant amounts of maize from Uganda and sorghum from Sudan. First, we convert cereal flour consumption into grain by assuming that 1 kg of flour is produced from 1.25 kg of raw grain. Second, following the assumption used by FAO/WFP, we approximate that post-harvest losses at 20 percent. Third, it is assumed that 55 percent of grain purchased by rural households is produced locally, while the rest is supplied by imports; for urban households, we assumed that purchases are mainly supported by imports. Finally, domestic grain production is defined as consumption met by households' own production, stocks, and 55 percent of total rural household's purchases.

and production for 34 individual crops and the corresponding prices for them. The prices5 used in the calculation are averaged from individual households' self-reported information in the NBHS 2009. Limited by the lack of geo-referenced household identification in 2009 NBHS, we only calculate realized crop values at the state level.

Assessing Agricultural Potential in South Sudan – A Spatial Analysis Method 147

62.6 percent of national territory). For simplicity, we hereafter refer to both "trees with

a. If a pixel C (current cropland) belongs to Type HH area and is surrounded by pixels under "tree land" then the 8 immediate adjoin pixels (1s in Figure 5), 16 pixels (2s) immediately surrounding the pixels identified with 1s, and the 24 pixels (3s) immediately adjacent to the 2s are assumed to become cropland in the next five to ten

b. For HL and MH areas, cropland expansion is more modest. It only assumes the 8 pixels (1s) immediately adjoining pixel C and the 16 pixels (identified as 2s) to become

c. The expansion is even lower in ML and LH areas as it only considers the 8 pixels immediately adjoining pixel C in the projected cropland conversion. Finally, we assume

Hence, in the moderate expansion scenario and given that each pixel is roughly about 1 km2, the maximum possible conversion to cropland is 48 km2 in HH areas, 24 km2 in HL and MH areas, and 8 km2 in ML and LH areas. However, as current cropland areas are often connected, i.e., many pixels (C) are already adjacent each other, only those C pixels at the boundary areas are considered when their surrounded pixels under "tree land" become

> 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 5 5 4 3 3 3 3 3 3 3 4 5 5 4 3 2 2 2 2 2 3 4 5 5 4 3 2 **1 1 1** 2 3 4 5 5 4 3 2 **1 C 1** 2 3 4 5 5 4 3 2 **1 1 1** 2 3 4 5 5 4 3 2 2 2 2 2 3 4 5 5 4 3 3 3 3 3 3 3 4 5 5 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5

The high expansion scenario (Scenario 2) doubles the cropland expansion in the moderate

a. In HH area, pixels 1, 2, 3, 4, and 5 surrounding pixel C are assumed to be converted to

b. Pixels 1,2,3, and 4 surrounding C in HL and MH areas that are currently covered with

scenarios in the next five to ten years and is based on the following assumptions:

cropland if their current land use is characterized under "tree land";

"tree land" are assumed to be converted to cropland; and

that any "tree land" of the Type LL area will not become cropland in the future.

crops" and "tree land" as just "tree land" and use the following rules for expansion:

cropland in the future if they currently classified as "tree land"; and

years (i.e. all the 1s, 2s, and 3s are candidates);

candidates for cropland expansion in the scenario.

**Figure 1.** Illustration of cropland expansion at pixel level

Source: Authors' illustration.

The current value of crop production, which represents the "realized agriculture potential" in South Sudan, is only about US\$600 million. Crops, together with livestock and fishery products, make up about US\$800 million worth of total agricultural value, but still remains relatively low compared with that of neighbors. Given that the current cropland area is about 2.7 million ha, the average crop value per ha is US\$227.

Measured at household level, the total value is about \$630 per household, of which \$470 is from crops. The difference in per household agriculture value across states is large. For example, Western Equatoria, which has more lands allocated to high value crops, is the richest state with per household agricultural value close to US\$1,300. On the other hand, with less than US\$300, Unity, Northern Bahr Al Ghazal, and Western Bahr Al Ghazal have the lowest household agricultural values.

## **Cropland expansion**

The low agricultural value can be mainly associated with South Sudan's undeveloped cultivated lands for agricultural production. As previously discussed, the country has abundant land with favorable climatic and soil conditions suitable for crop production; hence there is considerable scope that unutilized land can be converted into crop land under certain necessary conditions. Based on LGP, population density, and type of current land use, we project the potential cropland expansion under a moderate (Scenario 1) and a high expansion scenario (Scenario 2) in five and ten year horizons. In the previous section, we distinguished three types of crop-related land use: the areas identified as "cropland", areas as "grass with crops", and areas as "trees with crops" (Table 1). To start with a benchmark of current cropland area, we assume that 10 percent of areas defined as "grass with crops" and "trees with crops" have been cultivated and thus contributed to the current agricultural production. Hence, the benchmark cropland is the sum of land use under original crop land area (24,779 thousand ha; see Table 1) plus 10 percent of land use each coming from "grass with crops" and "trees with crops". Based on this computation, it is estimated that cropland area is 2.7 million ha or 4.1 percent of total land area in the country (Table 3). However, areas under "grass with crops" are unlikely to become cropland due to unfavorable climatic and soil conditions. Thus, in the moderate expansion scenario, we adopt a hierarchical expansion model in which all land currently identified as "trees with crops" (2.6 percent of national land) is the first to be converted into cropland. Once this potential for expansion is exhausted, further expansion will occur in "tree land" areas (which currently accounts for

5 If the state's average price for particular crop is extremely low or high relative to other states, the national average price is used. If the price is either not available from the survey or extremely low compared with that in neighboring countries, then the lowest relevant price from Kenya or Ethiopia is used.

62.6 percent of national territory). For simplicity, we hereafter refer to both "trees with crops" and "tree land" as just "tree land" and use the following rules for expansion:

Hence, in the moderate expansion scenario and given that each pixel is roughly about 1 km2, the maximum possible conversion to cropland is 48 km2 in HH areas, 24 km2 in HL and MH areas, and 8 km2 in ML and LH areas. However, as current cropland areas are often connected, i.e., many pixels (C) are already adjacent each other, only those C pixels at the boundary areas are considered when their surrounded pixels under "tree land" become candidates for cropland expansion in the scenario.

Source: Authors' illustration.

146 Application of Geographic Information Systems

the lowest household agricultural values.

then the lowest relevant price from Kenya or Ethiopia is used.

**Cropland expansion**

and production for 34 individual crops and the corresponding prices for them. The prices5 used in the calculation are averaged from individual households' self-reported information in the NBHS 2009. Limited by the lack of geo-referenced household identification in 2009

The current value of crop production, which represents the "realized agriculture potential" in South Sudan, is only about US\$600 million. Crops, together with livestock and fishery products, make up about US\$800 million worth of total agricultural value, but still remains relatively low compared with that of neighbors. Given that the current cropland area is

Measured at household level, the total value is about \$630 per household, of which \$470 is from crops. The difference in per household agriculture value across states is large. For example, Western Equatoria, which has more lands allocated to high value crops, is the richest state with per household agricultural value close to US\$1,300. On the other hand, with less than US\$300, Unity, Northern Bahr Al Ghazal, and Western Bahr Al Ghazal have

The low agricultural value can be mainly associated with South Sudan's undeveloped cultivated lands for agricultural production. As previously discussed, the country has abundant land with favorable climatic and soil conditions suitable for crop production; hence there is considerable scope that unutilized land can be converted into crop land under certain necessary conditions. Based on LGP, population density, and type of current land use, we project the potential cropland expansion under a moderate (Scenario 1) and a high expansion scenario (Scenario 2) in five and ten year horizons. In the previous section, we distinguished three types of crop-related land use: the areas identified as "cropland", areas as "grass with crops", and areas as "trees with crops" (Table 1). To start with a benchmark of current cropland area, we assume that 10 percent of areas defined as "grass with crops" and "trees with crops" have been cultivated and thus contributed to the current agricultural production. Hence, the benchmark cropland is the sum of land use under original crop land area (24,779 thousand ha; see Table 1) plus 10 percent of land use each coming from "grass with crops" and "trees with crops". Based on this computation, it is estimated that cropland area is 2.7 million ha or 4.1 percent of total land area in the country (Table 3). However, areas under "grass with crops" are unlikely to become cropland due to unfavorable climatic and soil conditions. Thus, in the moderate expansion scenario, we adopt a hierarchical expansion model in which all land currently identified as "trees with crops" (2.6 percent of national land) is the first to be converted into cropland. Once this potential for expansion is exhausted, further expansion will occur in "tree land" areas (which currently accounts for

5 If the state's average price for particular crop is extremely low or high relative to other states, the national average price is used. If the price is either not available from the survey or extremely low compared with that in neighboring countries,

NBHS, we only calculate realized crop values at the state level.

about 2.7 million ha, the average crop value per ha is US\$227.

**Figure 1.** Illustration of cropland expansion at pixel level

The high expansion scenario (Scenario 2) doubles the cropland expansion in the moderate scenarios in the next five to ten years and is based on the following assumptions:

c. In ML and LH areas, only the pixels 1,2, and 3 are assumed to become cropland if currently part of "tree land" area.

Assessing Agricultural Potential in South Sudan – A Spatial Analysis Method 149

As expected, most cropland expansion occurred in areas with high agricultural potential. The areas under Type HH, HL, and MH would collectively expand from the current 53 percent to 65 percent of total cropland area. At the state level, the largest expansion into new crop land is expected in Western Bahr el Ghazal and the three Equatorial states (Map 4). Among the livelihood zones, there is huge potential for crop area expansion in Greenbelt

We also calculate the change in per capita cropland size under the moderate expansion scenario, assuming a 2.5 percent annual population growth rate. If the expansion occurs in five years, the per capita cropland size will increase from the current national average of 0.32 ha to 0.66 ha. If the expansion would take ten years, the land size will increase to 0.59 ha. In either 5- or 10-year simulation, only Western Bahr el Gazal and Western Equatoria

While the rate of cropland expansion is already rapid in Scenario 1, the per capita cropland would still be lower than in neighboring countries. Hence, we consider Scenario 2 that doubles the rate of expansion under the first scenario. Under this more aggressive scenario, there would be a 3.5-fold increase in cropland area of 9.2 million ha (accounting for 14.3 percent of national land). The share of tree land in total land will decline to 55 percent from the current 63 percent. The per capita cropland area under the high expansion scenario would correspondingly increase to 1.0 ha/pc if the expansion is achieved in the next five

Land use categories Area (in 1000 ha) Share of total land (%)

(1) Other land use includes Flood land, Water and rock, and Urban as categorized originally in Table 1.

10 percent of land use under "grass with crops" and 10 percent of land use under "trees with crops".

(2) Cropland under "Current" is the sum of land use under original crop land area (24,779 million ha; see Table 1) plus

The increase in cultivated areas through cropland expansion in both scenarios lead to higher agricultural output, and consequently to higher value of agricultural production. Even under the modest cropland expansion (Scenario 1), the value of total agricultural output (including crops, livestock, and fisheries) becomes 2.4 times higher (about US\$ 2 billion) than the current US\$ 800 million. It is expected that the largest increase will come from the three Equatorial states, Western Bahr el Ghazal, and Warrap. In the high expansion

Cropland 2,680.9 6,267.4 9,237.4 4.1 9.7 14.3 Trees with crops 1,536.6 0.0 0.0 2.4 0.0 0.0 Tree land 40,526.9 38,477.1 35,507.1 62.6 59.5 54.9 Grass with crops 292.6 292.6 292.6 0.5 0.5 0.5 Grass 9,633.8 9.633.8 9,633.8 14.9 14.9 14.9 Other land use 10,017.3 10,017.3 10,017.3 15.5 15.5 15.5 Total 64,688.3 64,688.3 64,688.3 100.0 100.0 100.0

Current Scenario 1 Scenario 2 Current Scenario 1 Scenario 2

and Western Flood Plains.

Source: Authors' calculations.

**Table 3.** Land expansions in the two scenarios

Note:

will reach cropland size of at least 1.0 ha per capita.

years and 0.87 ha/pc if expansion takes place in the next 10 years.

The resulting cropland expansion of both scenarios is presented in Map 4. It should be noted that the precision and accuracy of the potential cropland expansion are hindered by the lack of additional location-specific information and inability to verify the estimates at the ground level. Moreover, realizing the agricultural potential of new cropland depends on many other important factors such as public investments and policies, which can complicate the process and hence are not considered in the projections. Also, additional factors such as access to markets, land and forest policy regulations, as well as access to resources (tools and labor) required for land clearing and tree cutting, will determine the extent the actual extent of expansion.

Source: Authors' estimates

**Map 4.** Cropland expansion under the two scenarios

We focus on the moderate expansion scenario first. Holding other factors constant, cropland area will increase by 2.3 times, from the current 2.7 million ha to 6.3 million ha (Table 3). Cropland becomes 9.7 percent of national total land, up from the current (base) of 4.1 percent. While this increase is significant, it is still far below the agricultural potential assessed by the GOSS, which assesses that 50 percent of South Sudan's land surface is prime agricultural land (GOSS 2010). The share of "tree land" in total area will only slightly decline from the current 63 percent to 60 percent (Table 3).

As expected, most cropland expansion occurred in areas with high agricultural potential. The areas under Type HH, HL, and MH would collectively expand from the current 53 percent to 65 percent of total cropland area. At the state level, the largest expansion into new crop land is expected in Western Bahr el Ghazal and the three Equatorial states (Map 4). Among the livelihood zones, there is huge potential for crop area expansion in Greenbelt and Western Flood Plains.

We also calculate the change in per capita cropland size under the moderate expansion scenario, assuming a 2.5 percent annual population growth rate. If the expansion occurs in five years, the per capita cropland size will increase from the current national average of 0.32 ha to 0.66 ha. If the expansion would take ten years, the land size will increase to 0.59 ha. In either 5- or 10-year simulation, only Western Bahr el Gazal and Western Equatoria will reach cropland size of at least 1.0 ha per capita.

While the rate of cropland expansion is already rapid in Scenario 1, the per capita cropland would still be lower than in neighboring countries. Hence, we consider Scenario 2 that doubles the rate of expansion under the first scenario. Under this more aggressive scenario, there would be a 3.5-fold increase in cropland area of 9.2 million ha (accounting for 14.3 percent of national land). The share of tree land in total land will decline to 55 percent from the current 63 percent. The per capita cropland area under the high expansion scenario would correspondingly increase to 1.0 ha/pc if the expansion is achieved in the next five years and 0.87 ha/pc if expansion takes place in the next 10 years.

Source: Authors' calculations.

Note:

148 Application of Geographic Information Systems

Source: Authors' estimates

**Map 4.** Cropland expansion under the two scenarios

decline from the current 63 percent to 60 percent (Table 3).

currently part of "tree land" area.

c. In ML and LH areas, only the pixels 1,2, and 3 are assumed to become cropland if

The resulting cropland expansion of both scenarios is presented in Map 4. It should be noted that the precision and accuracy of the potential cropland expansion are hindered by the lack of additional location-specific information and inability to verify the estimates at the ground level. Moreover, realizing the agricultural potential of new cropland depends on many other important factors such as public investments and policies, which can complicate the process and hence are not considered in the projections. Also, additional factors such as access to markets, land and forest policy regulations, as well as access to resources (tools and labor) required for land clearing

We focus on the moderate expansion scenario first. Holding other factors constant, cropland area will increase by 2.3 times, from the current 2.7 million ha to 6.3 million ha (Table 3). Cropland becomes 9.7 percent of national total land, up from the current (base) of 4.1 percent. While this increase is significant, it is still far below the agricultural potential assessed by the GOSS, which assesses that 50 percent of South Sudan's land surface is prime agricultural land (GOSS 2010). The share of "tree land" in total area will only slightly

and tree cutting, will determine the extent the actual extent of expansion.

(1) Other land use includes Flood land, Water and rock, and Urban as categorized originally in Table 1.

(2) Cropland under "Current" is the sum of land use under original crop land area (24,779 million ha; see Table 1) plus 10 percent of land use under "grass with crops" and 10 percent of land use under "trees with crops".

**Table 3.** Land expansions in the two scenarios

The increase in cultivated areas through cropland expansion in both scenarios lead to higher agricultural output, and consequently to higher value of agricultural production. Even under the modest cropland expansion (Scenario 1), the value of total agricultural output (including crops, livestock, and fisheries) becomes 2.4 times higher (about US\$ 2 billion) than the current US\$ 800 million. It is expected that the largest increase will come from the three Equatorial states, Western Bahr el Ghazal, and Warrap. In the high expansion

scenario, the potential agricultural production value reaches US\$2.8 billion but is still far below the level of output produced in neighboring countries.

Assessing Agricultural Potential in South Sudan – A Spatial Analysis Method 151

South Sudan, the world's newest nation, has a huge agricultural potential that can be leveraged to improve the national economy and household living standards. The country's endowment of favorable land, water, and weather conditions makes 70 percent of land suitable for agriculture. Yet, less than four percent of total land (about 2.7 million ha) is currently cultivated while more than 80 percent is still under natural vegetation (e.g. trees, shrubs, grass). The production system remains primarily subsistence in nature and crop yield is low. Our analysis shows that the current total value of agriculture production (i.e. "realized potential") only amounts to about US\$800 million (US\$ 600 million from crops) or less than US\$300 per hectare, which is much lower than that of its neighbouring countries. Even with an extremely low population density (13 persons per km2), per capita crop area is only at 0.3

In this context, the newly independent country faces challenges in providing enough food for her population that is expected to increase in the short run due to the re-integration of displaced people. Obstacles in developing the country's competitiveness in regional and global markets in the longer term also need to be overcome. In order to have a more realistic agricultural development strategy and investment priorities, it is necessary to understand the country's current agricultural situation and potential for improvement in the near future. We employ a GIS-based analysis and come up with six agricultural potential typologies. HH, HL, and MH are best positioned to be developed, and more than half of current cropland areas fall under these categories. There is possibility of promoting intensive farming systems since areas with "high" and "medium" agricultural potential have population density greater than the 50/km2 threshold. However, there are also "high" agro-ecological potential areas with very low population density indicating the difficulty to

Incorporating these elements together, we then spatially estimate the agricultural potential value in the next five to ten years by simulating: (1) an increase in cultivated area though cropland expansion, and (2) crop yield improvement. If cropland areas expand to 6.3 million or 9.2 million hectares, size of per capita land holding will significantly increase, and consequently results in higher value of agricultural production relative to the current "realized potential". However, the potential agricultural value even in the high expansion

Catching up with crop yield levels achieved by its neighboring countries will be the most important approach to realize agricultural potential. Doubling the current average cereal yield of 0.95 ton/ha, along with moderate cropland expansion, will shoot up the value of agricultural production to US\$3.7 billion, a level that can overtake the current agricultural value in Uganda. Given that many challenges in cropland expansion, including high upfront costs of land clearing and low rural connectivity, yield improvement maybe a more effective

scenario is still far below the level of output produced in neighboring countries.

way to realize agricultural potential in South Sudan over the next years.

develop them with a smallholder farming system.

**4. Conclusion**

hectare.

## **Yield improvement**

Land expansion is only one of many ways to explore agricultural potential; another avenue is to increase land productivity which also happens to be low in South Sudan. In order to be at par with its neighbors' production levels, yield improvement is necessary. There is a huge gap between the county's actual farm yield and the biophysically achievable yield according to IIASA/FAO Agro-ecological Zone (AEZ) framework (Fischer et al. 2002). The average cereal yield is only about 0.95 ton/ha (FAO/WFP, 2011), but can actually be lower since the cropland area used in the FAO/WFP (2011) is much lower than the areas observed in Land Cover (FAO 2009). This average cereal yield is lower than Uganda where there is minimal use of tradeable inputs (1.6 tons/ha), as well as lower in places with disadvantageous agroecological conditions like Ethiopia (3 tons/ha) and Kenya (2 tons/ha). Such wide yield gap in South Sudan points to a large opportunity to increase average cereal yields.

We design four yield increase scenarios in which the average yield will increase by 50, 100, 200, and 300 percent in a period of 5 or 10 years. An increase by 50 percent is simulated to achieve the average level in Uganda, by 100 percent to attain Kenya's level, and by 200 percent to reach that of Ethiopia. While there is no neighboring country with a cereal yield of 6.0 ton/ha national wide (300 percent increase), such level is observed in certain parts of Ethiopia and Kenya.

Under Scenario 1 of land expansion, a 50 percent yield increase would increase the agricultural production value 3.5 times from the current value. This increase in agricultural value is also 45 percent higher than the increase achieved from Scenario 1 without yield improvement. Accordingly, the value of crop production per ha will grow from the current US\$227 to US\$340. If yields can increase by 100 percent to mirror the average levels in Kenya, the value of agricultural production in South Sudan (about US\$3.7 billion) will overtake the current value in Uganda and crop value per ha will be US\$453. Under the most aggressive scenario, with average yield increasing by 300 percent, the total agricultural value will reach US\$ 7.9 billion and US\$ 1,903/ha.

There are two caveats in our estimation of agricultural potential. First, we do not consider the price effect. At the present, food production of South Sudan is not enough for domestic demand. Urban consumption is primarily met by imports, and food aid is an important food source both for rural and urban households. Thus, we do not expect that a modest increase in crop production to cause an oversupply issue for the country in general. However, it is still possible that significant increases in crop yields, in the absence of opportunities to export surplus can create glut in certain areas during harvest season. When this happens, the prices for many crop products are expected to fall, which indicates that we may overestimate the agricultural potential. The second caveat is related to the livestock sector which we did not consider in the supply increase simulation although this sector also has a huge potential in the country. Without considering productivity increase in livestock production, we may significantly underestimate the agricultural potential.

## **4. Conclusion**

150 Application of Geographic Information Systems

**Yield improvement**

Ethiopia and Kenya.

value will reach US\$ 7.9 billion and US\$ 1,903/ha.

significantly underestimate the agricultural potential.

below the level of output produced in neighboring countries.

scenario, the potential agricultural production value reaches US\$2.8 billion but is still far

Land expansion is only one of many ways to explore agricultural potential; another avenue is to increase land productivity which also happens to be low in South Sudan. In order to be at par with its neighbors' production levels, yield improvement is necessary. There is a huge gap between the county's actual farm yield and the biophysically achievable yield according to IIASA/FAO Agro-ecological Zone (AEZ) framework (Fischer et al. 2002). The average cereal yield is only about 0.95 ton/ha (FAO/WFP, 2011), but can actually be lower since the cropland area used in the FAO/WFP (2011) is much lower than the areas observed in Land Cover (FAO 2009). This average cereal yield is lower than Uganda where there is minimal use of tradeable inputs (1.6 tons/ha), as well as lower in places with disadvantageous agroecological conditions like Ethiopia (3 tons/ha) and Kenya (2 tons/ha). Such wide yield

gap in South Sudan points to a large opportunity to increase average cereal yields.

We design four yield increase scenarios in which the average yield will increase by 50, 100, 200, and 300 percent in a period of 5 or 10 years. An increase by 50 percent is simulated to achieve the average level in Uganda, by 100 percent to attain Kenya's level, and by 200 percent to reach that of Ethiopia. While there is no neighboring country with a cereal yield of 6.0 ton/ha national wide (300 percent increase), such level is observed in certain parts of

Under Scenario 1 of land expansion, a 50 percent yield increase would increase the agricultural production value 3.5 times from the current value. This increase in agricultural value is also 45 percent higher than the increase achieved from Scenario 1 without yield improvement. Accordingly, the value of crop production per ha will grow from the current US\$227 to US\$340. If yields can increase by 100 percent to mirror the average levels in Kenya, the value of agricultural production in South Sudan (about US\$3.7 billion) will overtake the current value in Uganda and crop value per ha will be US\$453. Under the most aggressive scenario, with average yield increasing by 300 percent, the total agricultural

There are two caveats in our estimation of agricultural potential. First, we do not consider the price effect. At the present, food production of South Sudan is not enough for domestic demand. Urban consumption is primarily met by imports, and food aid is an important food source both for rural and urban households. Thus, we do not expect that a modest increase in crop production to cause an oversupply issue for the country in general. However, it is still possible that significant increases in crop yields, in the absence of opportunities to export surplus can create glut in certain areas during harvest season. When this happens, the prices for many crop products are expected to fall, which indicates that we may overestimate the agricultural potential. The second caveat is related to the livestock sector which we did not consider in the supply increase simulation although this sector also has a huge potential in the country. Without considering productivity increase in livestock production, we may South Sudan, the world's newest nation, has a huge agricultural potential that can be leveraged to improve the national economy and household living standards. The country's endowment of favorable land, water, and weather conditions makes 70 percent of land suitable for agriculture. Yet, less than four percent of total land (about 2.7 million ha) is currently cultivated while more than 80 percent is still under natural vegetation (e.g. trees, shrubs, grass). The production system remains primarily subsistence in nature and crop yield is low. Our analysis shows that the current total value of agriculture production (i.e. "realized potential") only amounts to about US\$800 million (US\$ 600 million from crops) or less than US\$300 per hectare, which is much lower than that of its neighbouring countries. Even with an extremely low population density (13 persons per km2), per capita crop area is only at 0.3 hectare.

In this context, the newly independent country faces challenges in providing enough food for her population that is expected to increase in the short run due to the re-integration of displaced people. Obstacles in developing the country's competitiveness in regional and global markets in the longer term also need to be overcome. In order to have a more realistic agricultural development strategy and investment priorities, it is necessary to understand the country's current agricultural situation and potential for improvement in the near future. We employ a GIS-based analysis and come up with six agricultural potential typologies. HH, HL, and MH are best positioned to be developed, and more than half of current cropland areas fall under these categories. There is possibility of promoting intensive farming systems since areas with "high" and "medium" agricultural potential have population density greater than the 50/km2 threshold. However, there are also "high" agro-ecological potential areas with very low population density indicating the difficulty to develop them with a smallholder farming system.

Incorporating these elements together, we then spatially estimate the agricultural potential value in the next five to ten years by simulating: (1) an increase in cultivated area though cropland expansion, and (2) crop yield improvement. If cropland areas expand to 6.3 million or 9.2 million hectares, size of per capita land holding will significantly increase, and consequently results in higher value of agricultural production relative to the current "realized potential". However, the potential agricultural value even in the high expansion scenario is still far below the level of output produced in neighboring countries.

Catching up with crop yield levels achieved by its neighboring countries will be the most important approach to realize agricultural potential. Doubling the current average cereal yield of 0.95 ton/ha, along with moderate cropland expansion, will shoot up the value of agricultural production to US\$3.7 billion, a level that can overtake the current agricultural value in Uganda. Given that many challenges in cropland expansion, including high upfront costs of land clearing and low rural connectivity, yield improvement maybe a more effective way to realize agricultural potential in South Sudan over the next years.

## **Author details**

Xinshen Diao, Liangzhi You, Vida Alpuerto and Renato Folledo *International Food Policy Research Institute (IFPRI), Washington DC, USA*

## **Acknowledgement**

The chapter is the primary research output of a project funded by Africa Region of the World Bank. Tremendous support has been received from the government of South Sudan, WFP and FAO Sudan offices, researchers in many institutions/organizations in the country, and the World Bank South Sudan office. The principal authors accept responsibility for any errors.

**Chapter 9**

© 2012 Krigas et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Krigas et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**GIS and** *ex situ* **Plant Conservation**

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50525

category of plants usually includes:

Spanish endemics),

Karamplianis, 2009).

2010),

**1. Introduction**

species.

Nikos Krigas, Kimon Papadimitriou and Antonios D. Mazaris

Strategy for Plant Conservation [ESPC], 2009; Sharrock & Jones, 2009).

southern Spain, endemics of Peloponnese, southern Greece),

In the frame of the global efforts to halt the biodiversity loss by 2010 and with the aim to develop effective conservation strategies extending beyond 2010, stakeholders have recognized as a priority the *in situ* conservation (on site conservation) of target plant

Still, the rapid environmental changes including climate change, habitat loss and alteration, could pose some limitations on our ability to conserve target species effectively *in situ*  (Sharrock & Jones, 2009). As a result, conservation biologists, policy makers and managers acknowledge the importance of *ex situ* conservation of target plants in botanic gardens and seed banks as an essential back-up solution (Convention on Biological Diversity [CBD], 1992; Glawka et al 1994; Global Strategy for Plant Conservation [GSPC], 2002; European

For the *ex situ* plant conservation, target species mainly refer to plant taxa (species and subspecies) presenting a narrow distribution in the wild (see Krigas & Maloupa, 2008). This

i. Local endemics (endemics of a single mountaintop e.g. *Viola cephalonica* (Katsouni et al., 2009)*,* or endemics of a single island e.g. *Allium samothracicum* (Krigas, 2009)*,* or endemics of a group of nearby areas or islands e.g. *Thymus holosericeus* (Krigas et al.

ii. Regional endemics (endemics to small parts of a single country, e.g. endemics of

iii. National endemics or single-country endemics (e.g. Greek endemics, Italian endemics,

iv. Endemics to specified small geographical areas e.g. local Balkan endemics transcending the borders of neighbouring Balkan countries such as *Ranunculus cacuminis* (Krigas &

## **5. References**

## **GIS and** *ex situ* **Plant Conservation**

Nikos Krigas, Kimon Papadimitriou and Antonios D. Mazaris

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50525

**1. Introduction**

152 Application of Geographic Information Systems

Xinshen Diao, Liangzhi You, Vida Alpuerto and Renato Folledo *International Food Policy Research Institute (IFPRI), Washington DC, USA*

Special Report, 2008/2009, http://www.fao.org/giews.

Special Report, 2009/2010, http://www.fao.org/giews.

Special Report, 2010/2011, http://www.fao.org/giews.

development: a review. A report submitted to USAID.

Southern Sudan Livelihood Profiles. January 2006.

Southern Sudan: Estimates from the NBHS 2009. Juba: SSCCSE.

measure of urban concentration. Background paper for the WDR 2009.

The chapter is the primary research output of a project funded by Africa Region of the World Bank. Tremendous support has been received from the government of South Sudan, WFP and FAO Sudan offices, researchers in many institutions/organizations in the country, and the World Bank South Sudan office. The principal authors accept responsibility for any errors.

FAO, 1981. *Report of the Agro-Ecological Zones Project*, World Soil Resources Report No 48,

FAO/WFP. 2009. FAO/WFP crop and food security assessment mission to Southern Sudan,

FAO/WFP. 2010. FAO/WFP crop and food security assessment mission to Southern Sudan,

FAO/WFP. 2011. FAO/WFP crop and food security assessment mission to Southern Sudan,

Fischer, G., H.T. van Velthuizen, and F.O. Nachtergaele. 2002. Global agroecological assessment for agriculture in the 21st century: Methodology and results. RR-02-02.

Guvele, Cesar. 2009. Agricultural situation in Southern Sudan and the potential for

LandScan, 2008. http://www.ornl.gov/sci/landscan/index.shtml (accessed November 2010). Musinga, M., J.M. Gathuma, O. Engorok, and T. H. Dargie. 2010 The Livestock Sector in Southern Sudan: Results of a Value Chain Study of the Livestock Sector in Five States of Southern Sudan Covered by MDTF with a Focus on Red Meat. The Netherlands

Southern Sudan Centre for Census, Statistics and Evaluation (SSCCSE). 2010. Poverty in

Southern Sudan Centre for Census, Statistics and Evaluation (SSCCSE), Save the Children UK (SC UK), USAID Famine Early Warning Systems Network (FEWS NET). 2006.

Uchida, Hirotsugu, and Andrew Nelson. 2008. Agglomeration index: towards a new

World Food Program (WFP) and The Ministry of Transport and Roads, Government of Southern Sudan (GOSS). 2005. WFP Southern Sudan Emergency Road Rehabilitation Program - Socio-economic impact assessment: A report of 2004 and 2005 road

Laxenburg, Austria: International Institute for Applied System Analysis. GOSS. 2010. The Southern Sudan Food and Agriculture Policy Framework.

FAO, 2009. FAO Livestock Population Estimates, Oct 2009. FAOStat, Aug 2010

**Author details**

**Acknowledgement**

**5. References**

Vol.1-4, Rome, FAO

Development Organization.

rehabilitation activities

In the frame of the global efforts to halt the biodiversity loss by 2010 and with the aim to develop effective conservation strategies extending beyond 2010, stakeholders have recognized as a priority the *in situ* conservation (on site conservation) of target plant species.

Still, the rapid environmental changes including climate change, habitat loss and alteration, could pose some limitations on our ability to conserve target species effectively *in situ*  (Sharrock & Jones, 2009). As a result, conservation biologists, policy makers and managers acknowledge the importance of *ex situ* conservation of target plants in botanic gardens and seed banks as an essential back-up solution (Convention on Biological Diversity [CBD], 1992; Glawka et al 1994; Global Strategy for Plant Conservation [GSPC], 2002; European Strategy for Plant Conservation [ESPC], 2009; Sharrock & Jones, 2009).

For the *ex situ* plant conservation, target species mainly refer to plant taxa (species and subspecies) presenting a narrow distribution in the wild (see Krigas & Maloupa, 2008). This category of plants usually includes:

Other target species significant for the *ex situ* plant conservation may include plants which are rare in a certain area (e.g. Europe, Sharrock & Jones, 2009) or plants that are currently threatened with extinction at local, regional or global level i.e. taxa characterized as "Near Threatened", "Vulnerable", "Endangered" or "Critically Endangered" according to the IUCN (2001) criteria. However, given that almost 90% of the Europe's threatened plants are single-country endemics, it should be noted that most of the endemic plants are also threatened with extinction (Sharrock & Jones, 2009). Last but not least, other groups of socioeconomically valuable plants (e.g. saffron) and their progenitors (e.g. plants in the genus *Crocus*) may also be considered as target plants for the *ex situ* conservation (Fernández et al., 2011).

GIS and *ex situ* Plant Conservation 155

The GIS technology has also an enormous potential in seed conservation, particularly in targeting collecting needs for botanic gardens and conservation institutes and in identifying what, where and when to collect (Moat & Smith, 2003). Although concerns are raised regarding limitations and biases (Store & Kangas, 2001), some of the GIS applications have been used for their power to pinpoint by spatial niche modeling, new probable locations where rare endemic species may be found in the wild, thus permitting the search for new populations (Jarvis et al., 2005). The GIS could also be used for assessing the sensitivity of target species to climate change identifying potential distributions as well as vulnerable habitats and species (Vanderpoorten et al., 2006). In this way GIS may deliver important information that can drive seed banking priorities and design (Godefroid & Vanderborght,

Lately, a new application of GIS has been launched for the *ex situ* plant conservation; GIS was used to describe quantitatively and qualitatively the natural habitats of target plants in order to facilitate their propagation and transfer from the wild habitats to man-made

**2. GIS used for the description of the natural habitats of target species**

The geographical data associated with plant collections may be considered as a set of facts about the places in which the plants thrive. In the wild, target plants for *ex situ* conservation could thrive literally anywhere. Given the spatial and temporal heterogeneity of the environmental factors and the dissected topography of the landscape, the target plants may well originate in a variety of quite different habitats in which they are adapted to grow naturally. Hence, it seems quite difficult to be able to emulate their preferable conditions when trying to cultivate them in restricted man-made habitats such as botanic gardens (Krigas et al., 2010). A deeper understanding of the ecology and life cycle of such target species has been considered as a key issue towards a species-specific successful propagation

In this framework, the GIS can be used to offer a reliable, quantitative and qualitative description of the *in situ* habitat conditions preferred and/or tolerated by different target plant species in the wild. This novel GIS application is both ecologically meaningful and

To demonstrate this application a dataset was chosen including target plants originating from sites of varied landscapes that are also subjected to different climatic conditions (Fig. 1): the Aegean Archipelago, Crete, Ionian Islands, and Peloponnese (Greece, Southern Europe). The target plants included in this dataset fall into the conservation priorities of the Balkan Botanic Garden of Kroussia (BBGK, Krigas & Maloupa, 2008, Krigas et al., 2010). To date, the BBGK has organized several botanic expeditions all over Greece in order to collect appropriate plant material for propagation and *ex situ* conservation. All this plant material is currently maintained under *ex situ* conservation (ca. 25% of the known Greek flora which

useful in horticulture and *ex situ* plant conservation (Schulman & Lehvävirta, 2010).

includes at least 6.300 species and subspecies, Krigas et al., 2010).

habitats like botanic gardens (Krigas et al., 2010).

and cultivation method (Baskin & Baskin, 1988).

2010).

GIS has be given a role in analyzing potential and current spatial distribution of target species, locating and assessing the populations of target plant species and assemblages, measuring biodiversity, monitoring biodiversity patterns and identifying priorities for conservation and management (Iverson & Prasad, 1998; Salem, 2003; Powel et al., 2005; Pedersen et al., 2004).

Habitat evaluation or habitat modeling with the use of GIS has the potential to make a substantial contribution to conservation management of target species within an integrated approach and is suitable for setting conservation priorities at multiple spatial scales (Store & Jokimäki, 2003; Powel et al., 2005). GIS have also been used as a tool for specific conservation programmes including comparisons of ecological patterns between local and regional scales, selection of protected areas according to habitat suitability, analysis of the impact of alien species on endemic plants and selection of sites for representative seed collections of target species (Draper et al., 2003). Flexible GIS-based tools have also been developed to exploit static information of botanical collections in an attempt to evaluate species distributional ranges (Schulman et al., 2007; Loiselle et al., 2008), accounting for potential effect of climate change to predicted models (Loiselle et al., 2008). Other GIS-tools and distribution modeling methods have been applied to examine the hypothesis that wild and cultivated plants of certain species may occur in the same types of habitats (Allison et al., 2006).

Currently, the GIS technology has increasingly been used for predictive purposes in species re-introductions. Combining historical information from specimen labels with up-to-date environmental data, GIS can be used to identify the range of environmental conditions in which plants grow, offering an understanding of the ecological requirements of different species. The relevant environmental conditions can then be used to delimit areas of high, moderate or low survival probability (Sawkins, 1999 as cited in Moat & Smith, 2003; Powel et al., 2005). Recent GIS applications further include studies on the assessment of the importance of landscape connectivity, structure and configuration for native and non-native plant communities, dispersal and invasiveness abilities (Minor et al., 2009). Nevertheless, the most frequently encountered obstacle to the use of GIS technology in conservation planning is lack of data, especially distribution data of target species and digital vegetation maps of areas with conservation interest (Sawkins, 1999 as cited in Moat & Smith, 2003).

The GIS technology has also an enormous potential in seed conservation, particularly in targeting collecting needs for botanic gardens and conservation institutes and in identifying what, where and when to collect (Moat & Smith, 2003). Although concerns are raised regarding limitations and biases (Store & Kangas, 2001), some of the GIS applications have been used for their power to pinpoint by spatial niche modeling, new probable locations where rare endemic species may be found in the wild, thus permitting the search for new populations (Jarvis et al., 2005). The GIS could also be used for assessing the sensitivity of target species to climate change identifying potential distributions as well as vulnerable habitats and species (Vanderpoorten et al., 2006). In this way GIS may deliver important information that can drive seed banking priorities and design (Godefroid & Vanderborght, 2010).

154 Application of Geographic Information Systems

(Fernández et al., 2011).

Pedersen et al., 2004).

al., 2006).

Other target species significant for the *ex situ* plant conservation may include plants which are rare in a certain area (e.g. Europe, Sharrock & Jones, 2009) or plants that are currently threatened with extinction at local, regional or global level i.e. taxa characterized as "Near Threatened", "Vulnerable", "Endangered" or "Critically Endangered" according to the IUCN (2001) criteria. However, given that almost 90% of the Europe's threatened plants are single-country endemics, it should be noted that most of the endemic plants are also threatened with extinction (Sharrock & Jones, 2009). Last but not least, other groups of socioeconomically valuable plants (e.g. saffron) and their progenitors (e.g. plants in the genus *Crocus*) may also be considered as target plants for the *ex situ* conservation

GIS has be given a role in analyzing potential and current spatial distribution of target species, locating and assessing the populations of target plant species and assemblages, measuring biodiversity, monitoring biodiversity patterns and identifying priorities for conservation and management (Iverson & Prasad, 1998; Salem, 2003; Powel et al., 2005;

Habitat evaluation or habitat modeling with the use of GIS has the potential to make a substantial contribution to conservation management of target species within an integrated approach and is suitable for setting conservation priorities at multiple spatial scales (Store & Jokimäki, 2003; Powel et al., 2005). GIS have also been used as a tool for specific conservation programmes including comparisons of ecological patterns between local and regional scales, selection of protected areas according to habitat suitability, analysis of the impact of alien species on endemic plants and selection of sites for representative seed collections of target species (Draper et al., 2003). Flexible GIS-based tools have also been developed to exploit static information of botanical collections in an attempt to evaluate species distributional ranges (Schulman et al., 2007; Loiselle et al., 2008), accounting for potential effect of climate change to predicted models (Loiselle et al., 2008). Other GIS-tools and distribution modeling methods have been applied to examine the hypothesis that wild and cultivated plants of certain species may occur in the same types of habitats (Allison et

Currently, the GIS technology has increasingly been used for predictive purposes in species re-introductions. Combining historical information from specimen labels with up-to-date environmental data, GIS can be used to identify the range of environmental conditions in which plants grow, offering an understanding of the ecological requirements of different species. The relevant environmental conditions can then be used to delimit areas of high, moderate or low survival probability (Sawkins, 1999 as cited in Moat & Smith, 2003; Powel et al., 2005). Recent GIS applications further include studies on the assessment of the importance of landscape connectivity, structure and configuration for native and non-native plant communities, dispersal and invasiveness abilities (Minor et al., 2009). Nevertheless, the most frequently encountered obstacle to the use of GIS technology in conservation planning is lack of data, especially distribution data of target species and digital vegetation maps of areas with conservation interest (Sawkins, 1999 as cited in Moat & Smith, 2003).

Lately, a new application of GIS has been launched for the *ex situ* plant conservation; GIS was used to describe quantitatively and qualitatively the natural habitats of target plants in order to facilitate their propagation and transfer from the wild habitats to man-made habitats like botanic gardens (Krigas et al., 2010).

## **2. GIS used for the description of the natural habitats of target species**

The geographical data associated with plant collections may be considered as a set of facts about the places in which the plants thrive. In the wild, target plants for *ex situ* conservation could thrive literally anywhere. Given the spatial and temporal heterogeneity of the environmental factors and the dissected topography of the landscape, the target plants may well originate in a variety of quite different habitats in which they are adapted to grow naturally. Hence, it seems quite difficult to be able to emulate their preferable conditions when trying to cultivate them in restricted man-made habitats such as botanic gardens (Krigas et al., 2010). A deeper understanding of the ecology and life cycle of such target species has been considered as a key issue towards a species-specific successful propagation and cultivation method (Baskin & Baskin, 1988).

In this framework, the GIS can be used to offer a reliable, quantitative and qualitative description of the *in situ* habitat conditions preferred and/or tolerated by different target plant species in the wild. This novel GIS application is both ecologically meaningful and useful in horticulture and *ex situ* plant conservation (Schulman & Lehvävirta, 2010).

To demonstrate this application a dataset was chosen including target plants originating from sites of varied landscapes that are also subjected to different climatic conditions (Fig. 1): the Aegean Archipelago, Crete, Ionian Islands, and Peloponnese (Greece, Southern Europe). The target plants included in this dataset fall into the conservation priorities of the Balkan Botanic Garden of Kroussia (BBGK, Krigas & Maloupa, 2008, Krigas et al., 2010). To date, the BBGK has organized several botanic expeditions all over Greece in order to collect appropriate plant material for propagation and *ex situ* conservation. All this plant material is currently maintained under *ex situ* conservation (ca. 25% of the known Greek flora which includes at least 6.300 species and subspecies, Krigas et al., 2010).

GIS and *ex situ* Plant Conservation 157

a. The elevation data (DEM) was used for raster based terrain analysis, which resulted in

b. The DEM was further used as the base layer for the digitization of the vegetation zones

c. The rasters describing the spatial variations of precipitation, maximum and minimum temperatures from the WorldClim geodatabase (Guarino et al., 2002), were used in map algebra calculations for the production of a raster map which displays the spatial variation of the Emberger Pluviothermic (Ombrothermic) Quotient as: 2000\*P/(Tmax+Tmin)\*(Tmax-Tmin), where P: the layer of annual precipitation (mm), T: the layers of mean maximum (Tmax) and minimum (Tmin) monthly temperature of the warmest and coldest month in Kelvin degrees. Precipitation (P) Seasonality was calculated also, using the following map algebra expression: 100\* (Pw -Pd ) / Py, where Pw and Pd are the raster layers for the spatial variations of precipitation for the wettest and driest quarter of the year respectively, and Py the total annual precipitation (Fig. 1).

**Figure 2.** Methodology concept: The GPS data can be used for point sampling in a GIS environment regarding the collection sites of a target species over multiple polygon and raster layers extracted from selected geo-databases. This approach leads to the attribution of selected values regarding precipitation, land cover, terrain, topography, soil typology, temperature and climate to the wild habitats of a target species, resulting in a summarized fact sheet which reflects the ecological preferences of a target species

(e.g. *Thymus holosericeus* originating from the Ionian Islands, SW Greece).

slope and aspect maps of the Greek territory.

of Greece according to Mavromatis (1980).

**Figure 1.** Different target plants (n=256, each dot represents at least one botanic expedition and at least one target plant collected) from various sites originating from the selected area of Greece including the Aegean Archipelago, the Ionian Islands, Crete and Peloponnese (left). All plant material is currently maintained under *ex situ* cultivation at the Balkan Botanic Garden of Kroussia (BBGK). Raster maps displaying the variations of climatic conditions in Greece as expressed by the Emberger Pluviothermic Quotient (middle) and Precipitation Seasonality (right). All values have resulted from map algebra calculations after point sampling for the Greek territory regarding the collection sites of the selected target plants (n=256).

The positions of the collection sites of target plant species were captured in the wild using handheld GPS trackers. The obtained geographic coordinates were consequently imported as point layer into a Geographical Information System (GIS) and point sampling was performed for each one on multiple raster and polygon layers. In order to probe values for topography, terrain, soil, climatic, land cover and habitat attributes at the captured sites, the following datasets were selected (Fig. 2):

Additionally, part of the initial data was processed to produce more specific attributes for the collection sites of the target plants, as follows:

a. The elevation data (DEM) was used for raster based terrain analysis, which resulted in slope and aspect maps of the Greek territory.

156 Application of Geographic Information Systems

target plants (n=256).

following datasets were selected (Fig. 2):

72 soil parameters.

which were used for terrain and topography attributes.

description of climatic conditions (average values of 50 years).

soil types (CORINE soil classification EC, 1985).

the collection sites of the target plants, as follows:

**Figure 1.** Different target plants (n=256, each dot represents at least one botanic expedition and at least one target plant collected) from various sites originating from the selected area of Greece including the Aegean Archipelago, the Ionian Islands, Crete and Peloponnese (left). All plant material is currently maintained under *ex situ* cultivation at the Balkan Botanic Garden of Kroussia (BBGK). Raster maps displaying the variations of climatic conditions in Greece as expressed by the Emberger Pluviothermic Quotient (middle) and Precipitation Seasonality (right). All values have resulted from map algebra calculations after point sampling for the Greek territory regarding the collection sites of the selected

The positions of the collection sites of target plant species were captured in the wild using handheld GPS trackers. The obtained geographic coordinates were consequently imported as point layer into a Geographical Information System (GIS) and point sampling was performed for each one on multiple raster and polygon layers. In order to probe values for topography, terrain, soil, climatic, land cover and habitat attributes at the captured sites, the

a. Raster elevation data from SRTM (USGS 2004), with resolution of 1 km2 (30 arc second)

b. Soil data from the European Soil Database (ESDB) v2.0 (EC, 2004), which is composed of the Soil Geographical Database of Eurasia at a scale of 1: 1,000,000 (version 4 beta) and the Pedotransfer Rules Database (v2.0), with a raster resolution of 1 km2, presenting

c. Temperature and precipitation data from the WorldClim database (Guarino et al., 2002; Hijmans et al., 2005), with a raster resolution of 1km2, which has been used for the

d. Land cover data from CORINE (Coordination of Information on the Environment) comprehensive hierarchical vector geodatabase at a scale of 1: 100,000 (with a minimum mapping unit of 25ha) which present the spatial distribution of different landcover/land-use types (CORINE land cover classification, EC & ETC/LC, 1999) and

Additionally, part of the initial data was processed to produce more specific attributes for

**Figure 2.** Methodology concept: The GPS data can be used for point sampling in a GIS environment regarding the collection sites of a target species over multiple polygon and raster layers extracted from selected geo-databases. This approach leads to the attribution of selected values regarding precipitation, land cover, terrain, topography, soil typology, temperature and climate to the wild habitats of a target species, resulting in a summarized fact sheet which reflects the ecological preferences of a target species (e.g. *Thymus holosericeus* originating from the Ionian Islands, SW Greece).

Based on this GIS application, simple or advanced ecological fact sheets may be constructed for different target species which actually reveal their preferences in the wild (Fig. 2, 3, 4). An ecological fact sheet illustrates the ecological profile of a wild growing target plant and can be designed in a way to include different kind of information such as: **Vegetation zone** (different types), **Climatic data** which is a combination of **Precipitation and Temperatures** (for the different sites: total annual and annual range of precipitation, driest and wettest month, minimum, mean and maximum temperatures, annual and diurnal temperature range, isothermality and seasonality, Emberger pluviothermic quotient), **Topographic** (for the different sites: elevation, aspect, slope), **Habitat** (bibliographic, field notes), **Land Cover Types** (for the different sites: CORINE classification in three levels) and **Geological-Pedological data** (for the different sites: Food and Agriculture Organization's [FAO] soil classification, World Reference Base Soil classification, dominant parent material in three levels, depth to rock, textural class, topsoil base saturation, subsoil water capacity) (see Fig. 2, 3, 4).

GIS and *ex situ* Plant Conservation 159

Chasmophyte in vertical rocky areas, crevices and gorges, on

limestone

**Figure 3.** Simple ecological fact sheet for a single-area endemic (*Silene cephallenia* subsp*. cephallenia)* produced after linking its collection data with those of geodatabases. *S. cephallenia* subsp*. cephallenia* is found exclusively at Poros gorge in Cephalonia (Ionian Islands, SW Greece), is protected by the Greek Presidential Decree 67/1981 and recently it has been included in the Red Data Book of Rare and Threatened Plants of Greece as ''Critically Endangered'' (Karagianni et al., 2009) and in Annex 2 of the

European Threatened Species (Sharrock & Jones, 2009).

Additionally, other information may also be associated with the ecological description of the wild habitat of target species such as **Taxonomic data** i.e. taxon's name, family, accession number (ASN) given, **Collection data** (e.g. geographical coordinates, description of state, province, exact locality etc), **Conservation assessment** (e.g. ranking according to priorities assigned), and **Mother Plantations** (number of total plant accessions or number of records). It should be mentioned that when the number of records introduced in GIS for a specific information field are >1, then any quantitative information may be described by the mean value and its standard deviation (± SD) (see Fig. 2 and 3).

## **3. GIS-facilitated development of protocols for** *ex situ* **conservation of target species**

Considering the recent challenges in regional conservation planning, it becomes apparent that there is an urgent need for increased applied research in order to develop propagation and cultivation protocols for target plants threatened with extinction, towards species recovery and populations' reinforcements (Bunn et al. 2011, Maunder et al. 2001 Sarasan et al. 2006). Moreover, the GSPC (2002) and the ESPC (2009) have included this urgent need under their conservation targets at European and global level (Target 8). The GIS may serve such a need and may help at the development of species-specific propagation protocols and *ex situ*  cultivation guidelines regarding target species. Such methodologies could also provide basic information and criteria for prioritizing collections of threaten or rare species based on their distributional patterns, population status and the genetic and/or geographic representation of current seed bank collections (Draper et al., 2003; Farnsworth et al., 2006) or even prioritizing sites for seed collections (Jarvis et al., 2005; Ramírez-Villegas et al., 2010).

## **3.1. GIS and seed germination**

The GIS may be used to facilitate the germination of seeds collected from the wild in plant propagation studies as shown by Krigas et al. (2010)*.* This study included plants originating at the Ionian Islands, south-western Greece and provided some example-cases.

Based on this GIS application, simple or advanced ecological fact sheets may be constructed for different target species which actually reveal their preferences in the wild (Fig. 2, 3, 4). An ecological fact sheet illustrates the ecological profile of a wild growing target plant and can be designed in a way to include different kind of information such as: **Vegetation zone** (different types), **Climatic data** which is a combination of **Precipitation and Temperatures** (for the different sites: total annual and annual range of precipitation, driest and wettest month, minimum, mean and maximum temperatures, annual and diurnal temperature range, isothermality and seasonality, Emberger pluviothermic quotient), **Topographic** (for the different sites: elevation, aspect, slope), **Habitat** (bibliographic, field notes), **Land Cover Types** (for the different sites: CORINE classification in three levels) and **Geological-Pedological data** (for the different sites: Food and Agriculture Organization's [FAO] soil classification, World Reference Base Soil classification, dominant parent material in three levels, depth to rock,

Additionally, other information may also be associated with the ecological description of the wild habitat of target species such as **Taxonomic data** i.e. taxon's name, family, accession number (ASN) given, **Collection data** (e.g. geographical coordinates, description of state, province, exact locality etc), **Conservation assessment** (e.g. ranking according to priorities assigned), and **Mother Plantations** (number of total plant accessions or number of records). It should be mentioned that when the number of records introduced in GIS for a specific information field are >1, then any quantitative information may be described by the mean

**3. GIS-facilitated development of protocols for** *ex situ* **conservation of**

Considering the recent challenges in regional conservation planning, it becomes apparent that there is an urgent need for increased applied research in order to develop propagation and cultivation protocols for target plants threatened with extinction, towards species recovery and populations' reinforcements (Bunn et al. 2011, Maunder et al. 2001 Sarasan et al. 2006). Moreover, the GSPC (2002) and the ESPC (2009) have included this urgent need under their conservation targets at European and global level (Target 8). The GIS may serve such a need and may help at the development of species-specific propagation protocols and *ex situ*  cultivation guidelines regarding target species. Such methodologies could also provide basic information and criteria for prioritizing collections of threaten or rare species based on their distributional patterns, population status and the genetic and/or geographic representation of current seed bank collections (Draper et al., 2003; Farnsworth et al., 2006) or even prioritizing

The GIS may be used to facilitate the germination of seeds collected from the wild in plant propagation studies as shown by Krigas et al. (2010)*.* This study included plants originating

textural class, topsoil base saturation, subsoil water capacity) (see Fig. 2, 3, 4).

value and its standard deviation (± SD) (see Fig. 2 and 3).

sites for seed collections (Jarvis et al., 2005; Ramírez-Villegas et al., 2010).

at the Ionian Islands, south-western Greece and provided some example-cases.

**target species**

**3.1. GIS and seed germination**

**Figure 3.** Simple ecological fact sheet for a single-area endemic (*Silene cephallenia* subsp*. cephallenia)* produced after linking its collection data with those of geodatabases. *S. cephallenia* subsp*. cephallenia* is found exclusively at Poros gorge in Cephalonia (Ionian Islands, SW Greece), is protected by the Greek Presidential Decree 67/1981 and recently it has been included in the Red Data Book of Rare and Threatened Plants of Greece as ''Critically Endangered'' (Karagianni et al., 2009) and in Annex 2 of the European Threatened Species (Sharrock & Jones, 2009).

GIS and *ex situ* Plant Conservation 161

Generally, the appropriate season for germination trials inside the greenhouse was selected by comparing the seasonal temperatures profiles of the local greenhouse used with those of the wild habitat of the target species (Ionian Islands). The season deemed as more appropriate was characterized by temperature ranges that could easily mimic those

To illustrate an example, the GIS-derived temperature profiles for the wild habitat of *Silene cephallenia* subsp. *cephallenia* (Figs. 3, 5) have dictated the selection of temperatures to test for seed germination, leading to a germination success of 64% (Krigas et al., 2010). Additionally, its ecological profile has explain the fact that seed germination was inhibited at 10oC, whereas seed germination was considerably increased in only 7 days when higher

temperature (21oC±1) was applied, in an attempt to emulate natural conditions.

**Figure 5.** Monthly variations of the climatic conditions (average values of 50 years for precipitation, minimum and maximum temperature) at the original collection sites of *Silene cephallenia* subsp.

Basically, the asexual propagation and the rooting trials of softwood cuttings from plants are performed in greenhouse conditions using a few individuals to produce large amounts of genetically identical plants. Lately, it has been shown that the GIS may also be used to facilitate the asexual plant propagation by cuttings (Krigas et al., 2010). For example, the GIS-derived data for the wild habitat of *Thymus holosericeus* demonstrated that early spring was the most appropriate season for the rooting trials of this target species (Krigas et al., 2010). During early spring, the temperatures of the selected greenhouse in their case have emulated in the best way the temperatures prevailing in the natural habitat of the target plant. In this study, during

*cephallenia* derived from its ecological fact sheet*.*

**3.2. GIS and asexual plant propagation by cuttings**

prevailing in the natural environment of the target species.

**Figure 4.** Advanced ecological fact sheet of *Origanum dictamnus* produced after linking data for 18 collection sites with those of geodatabases, enriched with thematic maps displaying the spatial variations of multiple ecological attributes over the area of Crete (the 18 original collection sites are distributed in three main areas shown with yellow circles). *O. dictamnus* is restricted to Crete (endemic), is protected by the Greek Presidential Decree 67/1981, Bern Convention and the European Directive 92/43/EEC, it has been included in the Red Data Book of Rare and Threatened Plants of Greece as ''Vulnerable" (Turland, 1995) and in Annex 2 of the European Threatened Species (Sharrock & Jones, 2009).

Generally, the appropriate season for germination trials inside the greenhouse was selected by comparing the seasonal temperatures profiles of the local greenhouse used with those of the wild habitat of the target species (Ionian Islands). The season deemed as more appropriate was characterized by temperature ranges that could easily mimic those prevailing in the natural environment of the target species.

160 Application of Geographic Information Systems

Chasmop hyte in vertical rocky areas, crevices and gorges, on limestone

**Figure 4.** Advanced ecological fact sheet of *Origanum dictamnus* produced after linking data for 18 collection sites with those of geodatabases, enriched with thematic maps displaying the spatial variations of multiple ecological attributes over the area of Crete (the 18 original collection sites are distributed in three main areas shown with yellow circles). *O. dictamnus* is restricted to Crete (endemic), is protected by the Greek Presidential Decree 67/1981, Bern Convention and the European Directive 92/43/EEC, it has been included in the Red Data Book of Rare and Threatened Plants of Greece as ''Vulnerable" (Turland, 1995)

and in Annex 2 of the European Threatened Species (Sharrock & Jones, 2009).

To illustrate an example, the GIS-derived temperature profiles for the wild habitat of *Silene cephallenia* subsp. *cephallenia* (Figs. 3, 5) have dictated the selection of temperatures to test for seed germination, leading to a germination success of 64% (Krigas et al., 2010). Additionally, its ecological profile has explain the fact that seed germination was inhibited at 10oC, whereas seed germination was considerably increased in only 7 days when higher temperature (21oC±1) was applied, in an attempt to emulate natural conditions.

**Figure 5.** Monthly variations of the climatic conditions (average values of 50 years for precipitation, minimum and maximum temperature) at the original collection sites of *Silene cephallenia* subsp. *cephallenia* derived from its ecological fact sheet*.*

### **3.2. GIS and asexual plant propagation by cuttings**

Basically, the asexual propagation and the rooting trials of softwood cuttings from plants are performed in greenhouse conditions using a few individuals to produce large amounts of genetically identical plants. Lately, it has been shown that the GIS may also be used to facilitate the asexual plant propagation by cuttings (Krigas et al., 2010). For example, the GIS-derived data for the wild habitat of *Thymus holosericeus* demonstrated that early spring was the most appropriate season for the rooting trials of this target species (Krigas et al., 2010). During early spring, the temperatures of the selected greenhouse in their case have emulated in the best way the temperatures prevailing in the natural habitat of the target plant. In this study, during

propagation the rooting substrate's temperature was kept at 18-21°C in order to accelerate rooting as indicated from previous experience (Maloupa et al., 2008). Nevertheless, the air temperature was kept between 18-25°C, in an attempt to emulate as best as possible the original conditions of the natural habitat which were indicated by the GIS. Furthermore, the relative humidity was kept at 80% for the first 7 days and was reduced gradually to 50% during the second week of rooting. This was followed due to the fact that *Th. holosericeus* is a xerophytic species that grows in areas of very low subsoil water capacity, especially during dry months (Fig. 2). In the case of *Th. holosericeus*, the combination of GIS data with previous experience on rooting of other species of the same genus raised propagation success nearly by 90% (from 45% to 80% rooting).

GIS and *ex situ* Plant Conservation 163

sites seemed more favorable than others for the accommodation of *A. occulta* plants (a site with temperature range as close as possible to the natural temperature range of the

From the above mentioned and when taking into account the associated technical information (see Krigas et al., 2010, Grigoriadou et al., 2011) it becomes apparent that GIS may be used for the development of effective protocols concerning the propagation and

Regardless the method used for plant propagation (cuttings, seeds or *in vitro* techniques), the young individuals produced (plantlets or seedlings) require hardening and acclimatization before their *ex situ* conservation. In this sense, the GIS-derived ecological profiles of the target plants could be used to provide specific guidelines for their effective *ex*

**4.1. Cultivation guidelines regarding soil media, potting volume, texture, pH and**

In general, it is known that for the successful *ex situ* cultivation of target plants, the plant medium to be used must be similar to that of the substrate in the plant's natural habitat, providing similar root aeration and drainage conditions. This is quite important since improved drainage conditions during cultivation can actually inhibit fungal disease risks, induce greater rooting depth and enhance general plant health and vigor (Brady & Weil,

The GIS-derived ecological profile for the wild habitat of *Silene cephallenia* subsp. *cephallenia* (Fig. 3) showed that the plants originally grow on "Calcaric Lithosol" according to the FAO (1985) classification or according to the European Soil Database, on "Calcaric Leptosol (mountainous), shallow (<25 cm) or extremely gravelly soils directly over continuous rock or soils having <20% (by volume) fine earth material" (Krigas et al., 2010). In an attempt to emulate the natural habitat during hardening and acclimatization of plantlets raised from seeds (seedlings), the type of commercial peat used in BBGK for transplantation contained lime and by adding vermiculite, a good approximation of its originally drained soil type was achieved. This fact has actually allowed the plantlets of *S. cephallenia* subsp. *cephallenia*

For the further *ex situ* cultivation of *Silene cephallenia* subsp. *cephallenia* and *Thymus holosericeous*, the GIS-derived ecological profiles (Fig. 2, 3) indicated that the basic soil group in which their wild habitats originate from is classified as ''Calcaric Lithosol'' (stony calcareous with a high concentration of MgCO3 and >15% free CaCO3, developed on dolomitic limestone). Such a substratum is characterized by a good drainage capacity, neutral to alkaline soil pH, low depth and a high ratio of stones to fine earth. The above

**4. GIS-derived ecological profiles and guidelines for the** *ex situ*

initial cultivation of plant material derived from target species.

to continue growing without problems observed.

wild habitat was chosen).

**cultivation of target plants**

*situ* cultivation (Table 1).

**drainage**

2002).

## **3.3. GIS and** *in vitro* **plant propagation**

The attempts of *in vitro* propagation of rare and threatened plant species are often associated with two inherent problems which may hold back conservation efforts. First of all, there is frequently a lack of published methods regarding the propagation of a certain plant species and secondly, there is often a limited amount of experimental plant material which can be initially available due to scarce populations of limited size found in the wild (Bunn et al., 2011; Krogstrup et al., 2005). Taken these limitations into account, almost every attempt to propagate rare and threatened plants is of great importance but seems difficult to achieve.

Although sophisticated technologies and modern methods have been used to date for the *in vitro* plant propagation (Benson, 1999), the GIS has not been exploited as a tool. Lately, Grigoriadou et al. (2011) have shown how the GIS may also be used to serve the needs and procedures of *in vitro* plant propagation*.* Using a case-study plant i.e. *Achillea occulta* which is a local endemic of southern Peloponnese (Southern Greece) recently characterized as "Vulnerable" (Constantinidis & Kalpoutzakis 2009), the authors have used GIS in this study for the:

sites seemed more favorable than others for the accommodation of *A. occulta* plants (a site with temperature range as close as possible to the natural temperature range of the wild habitat was chosen).

## **4. GIS-derived ecological profiles and guidelines for the** *ex situ*  **cultivation of target plants**

162 Application of Geographic Information Systems

by 90% (from 45% to 80% rooting).

for the:

of plantlets.

**3.3. GIS and** *in vitro* **plant propagation**

propagation the rooting substrate's temperature was kept at 18-21°C in order to accelerate rooting as indicated from previous experience (Maloupa et al., 2008). Nevertheless, the air temperature was kept between 18-25°C, in an attempt to emulate as best as possible the original conditions of the natural habitat which were indicated by the GIS. Furthermore, the relative humidity was kept at 80% for the first 7 days and was reduced gradually to 50% during the second week of rooting. This was followed due to the fact that *Th. holosericeus* is a xerophytic species that grows in areas of very low subsoil water capacity, especially during dry months (Fig. 2). In the case of *Th. holosericeus*, the combination of GIS data with previous experience on rooting of other species of the same genus raised propagation success nearly

The attempts of *in vitro* propagation of rare and threatened plant species are often associated with two inherent problems which may hold back conservation efforts. First of all, there is frequently a lack of published methods regarding the propagation of a certain plant species and secondly, there is often a limited amount of experimental plant material which can be initially available due to scarce populations of limited size found in the wild (Bunn et al., 2011; Krogstrup et al., 2005). Taken these limitations into account, almost every attempt to propagate rare and threatened plants is of great importance but seems difficult to achieve.

Although sophisticated technologies and modern methods have been used to date for the *in vitro* plant propagation (Benson, 1999), the GIS has not been exploited as a tool. Lately, Grigoriadou et al. (2011) have shown how the GIS may also be used to serve the needs and procedures of *in vitro* plant propagation*.* Using a case-study plant i.e. *Achillea occulta* which is a local endemic of southern Peloponnese (Southern Greece) recently characterized as "Vulnerable" (Constantinidis & Kalpoutzakis 2009), the authors have used GIS in this study

a. Selection of temperatures for both greenhouse cultivation and *in vitro* cultures; the GISderived ecological profile for *A. occulta* dictated the selection of 22±2 oC day temperature and 15±2 oC night temperature at 16-h photoperiod to be used as most

*b.* Selection of appropriate period for acclimatization and transplanting of plantlets produced *in vitro;* after balancing out the temperature profiles of the wild habitat with those prevailing in the man-made site of the botanic garden (BBGK), the GIS revealed that spring temperatures were more suitable for the active growth and acclimatization

c. Selection of growing media and substrates for plantlets produced *in vitro*; the type of commercial peat used with the addition of vermiculite provided a very good imitation

of the natural soil conditions as indicated by the GIS-derived ecological profile. d. Selection of appropriate *ex situ* conservation sites for plants raised *in vitro*; after balancing out the temperature profiles of the wild habitat with those prevailing in the available man-made *ex situ* conservation sites of BBGK, the GIS revealed that specific

suitable for the development of the *in vitro* cultures.

From the above mentioned and when taking into account the associated technical information (see Krigas et al., 2010, Grigoriadou et al., 2011) it becomes apparent that GIS may be used for the development of effective protocols concerning the propagation and initial cultivation of plant material derived from target species.

Regardless the method used for plant propagation (cuttings, seeds or *in vitro* techniques), the young individuals produced (plantlets or seedlings) require hardening and acclimatization before their *ex situ* conservation. In this sense, the GIS-derived ecological profiles of the target plants could be used to provide specific guidelines for their effective *ex situ* cultivation (Table 1).

## **4.1. Cultivation guidelines regarding soil media, potting volume, texture, pH and drainage**

In general, it is known that for the successful *ex situ* cultivation of target plants, the plant medium to be used must be similar to that of the substrate in the plant's natural habitat, providing similar root aeration and drainage conditions. This is quite important since improved drainage conditions during cultivation can actually inhibit fungal disease risks, induce greater rooting depth and enhance general plant health and vigor (Brady & Weil, 2002).

The GIS-derived ecological profile for the wild habitat of *Silene cephallenia* subsp. *cephallenia* (Fig. 3) showed that the plants originally grow on "Calcaric Lithosol" according to the FAO (1985) classification or according to the European Soil Database, on "Calcaric Leptosol (mountainous), shallow (<25 cm) or extremely gravelly soils directly over continuous rock or soils having <20% (by volume) fine earth material" (Krigas et al., 2010). In an attempt to emulate the natural habitat during hardening and acclimatization of plantlets raised from seeds (seedlings), the type of commercial peat used in BBGK for transplantation contained lime and by adding vermiculite, a good approximation of its originally drained soil type was achieved. This fact has actually allowed the plantlets of *S. cephallenia* subsp. *cephallenia*  to continue growing without problems observed.

For the further *ex situ* cultivation of *Silene cephallenia* subsp. *cephallenia* and *Thymus holosericeous*, the GIS-derived ecological profiles (Fig. 2, 3) indicated that the basic soil group in which their wild habitats originate from is classified as ''Calcaric Lithosol'' (stony calcareous with a high concentration of MgCO3 and >15% free CaCO3, developed on dolomitic limestone). Such a substratum is characterized by a good drainage capacity, neutral to alkaline soil pH, low depth and a high ratio of stones to fine earth. The above

indicated that for the *ex situ* cultivation of these target plants, a potting medium of relative high pH would be required and a fair degree of drainage was deemed essential. To accomplish this, supplementary perlite, sand and/or fragmented stone were added in their growing media. For these plants, medium sized pots (4.5 l) were suggested as suitable and additional Mg dressing was added in order to match the original chemical composition of their natural habitats.

GIS and *ex situ* Plant Conservation 165

exploits previous general horticultural experience associated with the cultivation of other species with presumably similar needs. However, it is generally accepted that the design of an irrigation system for the *ex situ* cultivation of target plants should take into account the natural preferences of the species, the medium type and the irrigation frequency requirements. The latter not only depends on the specific plants in question, but it is also influenced by the growing season and the indoor or outdoor temperatures prevailing in the

The precipitation profiles derived from GIS for the target plants may indicate differences regarding the water requirements preferred and/or tolerated by each one of them in the wild. For example, the GIS-derived ecological profile of *A. occulta* published by Grigoriadou et al. (2011) for its *ex situ* conservation, indicated a watering regime equal to a mean total annual precipitation of 744 mm (7–136 mm per month) which was followed for the proper

Seasonal variation of the watering regime is equally important as the total amount of water offered to plants. To illustrate an example, the GIS-derived ecological profiles of *Thymus holosericeous* and *Silene cephallenia* subsp. *cephallenia* may be taken into account (Fig. 2, 3, 5, 6). It becomes evident that during the wettest and driest quarters of the year, *Thymus holosericeus*'s habitat receives from 40 to 504 mm, while that of *Silene cephallenia*  subsp. *cephallenia* from 26 to 476 mm. During autumn, no noticeable differences exist regarding mean monthly precipitation in the natural habitats of these plants. However, from January to August mean monthly precipitation is consistently higher in the intermediate altitude habitats of *Th. holosericeus*, while until autumn *S. cephallenia* subsp. *cephallenia* (which is naturally found close to sea level) receive comparatively lower

**Figure 6.** Precipitation profiles for the original collection sites of *Thymus holosericeus* and *Silene*

*cephallenia* subsp. *cephallenia* derived from their ecological fact sheets.

cultivation sites.

precipitation (Fig. 5, 6).

cultivation of the *in vitro* propagated plants.

By adopting these guidelines (Table 1), the natural habitat of the target plants was technically emulated with common commercial materials and the plants have adapted well and continued to grow without apparent problems (Krigas et al., 2010).

## **4.2. Cultivation guidelines regarding temperature range**

When cultivating plants from the wild to man-made habitats, the temperature conditions at the plant's growing site (nursery or outdoors) may often be quite different from those prevailing in the plant's original wild habitat. Although it is known that plants usually have a degree of tolerance for a wide range of temperatures, it is important to know the temperature range that a plant species may tolerate, in order to facilitate its growth by maintaining temperatures within these limits during cultivation.

The GIS-derived ecological profile indicated that *Silene cephallenia* subsp. *cephallenia* (Fig. 3, 5) may experience and/or tolerate in its wild habitat mean temperatures ranging from 6.7 oC to 32.2 oC throughout the year, while for *Thymus holosericeus* the mean yearly temperatures may range from 3.9 oC to 29.5 oC (Krigas et al., 2010 and Fig. 2).

For the acclimatization procedure of the seedlings of *S. cephallenia* subsp. *cephallenia* and of the plantlets of *Th. holosericeous* produced in BBGK, the selection of appropriate sites was achieved after balancing out the temperature profiles of the available areas for *ex situ* conservation in northern Greece with the natural range of temperatures of their wild habitats, as revealed with the use of GIS (Table 1). It was suggested that *Th. holosericeus* may be marginally stressed during hot summer days and short term measures could possibly be taken to avoid heat shock (e.g. periodical shading or translocation of mother plants to cooler conditions). However, high summer temperatures did not seem to be a constraint for *S. cephallenia* subsp. *cephallenia*. Moreover, it was suggested that accommodation in a protected greenhouse would be crucial for the winter survival of both plants in northern Greece (Krigas et al., 2010). Indeed, the propagated plants that were transferred indoors (greenhouse at sea level, with controlled winter indoor temperatures ranging from 5 to 25 oC), have actually shown increased height, leaf area and number of flowers during cultivation in comparison to those transplanted outdoors (Krigas et al., 2010).

## **4.3. Cultivation guidelines regarding watering regimes**

When transferring wild plants to man-made sites for their *ex situ* cultivation, the amount of water to be offered to them remains largely unknown. The watering regime followed is based merely on observations regarding the natural habitat of the species in question or exploits previous general horticultural experience associated with the cultivation of other species with presumably similar needs. However, it is generally accepted that the design of an irrigation system for the *ex situ* cultivation of target plants should take into account the natural preferences of the species, the medium type and the irrigation frequency requirements. The latter not only depends on the specific plants in question, but it is also influenced by the growing season and the indoor or outdoor temperatures prevailing in the cultivation sites.

164 Application of Geographic Information Systems

their natural habitats.

indicated that for the *ex situ* cultivation of these target plants, a potting medium of relative high pH would be required and a fair degree of drainage was deemed essential. To accomplish this, supplementary perlite, sand and/or fragmented stone were added in their growing media. For these plants, medium sized pots (4.5 l) were suggested as suitable and additional Mg dressing was added in order to match the original chemical composition of

By adopting these guidelines (Table 1), the natural habitat of the target plants was technically emulated with common commercial materials and the plants have adapted well

When cultivating plants from the wild to man-made habitats, the temperature conditions at the plant's growing site (nursery or outdoors) may often be quite different from those prevailing in the plant's original wild habitat. Although it is known that plants usually have a degree of tolerance for a wide range of temperatures, it is important to know the temperature range that a plant species may tolerate, in order to facilitate its growth by

The GIS-derived ecological profile indicated that *Silene cephallenia* subsp. *cephallenia* (Fig. 3, 5) may experience and/or tolerate in its wild habitat mean temperatures ranging from 6.7 oC to 32.2 oC throughout the year, while for *Thymus holosericeus* the mean yearly temperatures

For the acclimatization procedure of the seedlings of *S. cephallenia* subsp. *cephallenia* and of the plantlets of *Th. holosericeous* produced in BBGK, the selection of appropriate sites was achieved after balancing out the temperature profiles of the available areas for *ex situ* conservation in northern Greece with the natural range of temperatures of their wild habitats, as revealed with the use of GIS (Table 1). It was suggested that *Th. holosericeus* may be marginally stressed during hot summer days and short term measures could possibly be taken to avoid heat shock (e.g. periodical shading or translocation of mother plants to cooler conditions). However, high summer temperatures did not seem to be a constraint for *S. cephallenia* subsp. *cephallenia*. Moreover, it was suggested that accommodation in a protected greenhouse would be crucial for the winter survival of both plants in northern Greece (Krigas et al., 2010). Indeed, the propagated plants that were transferred indoors (greenhouse at sea level, with controlled winter indoor temperatures ranging from 5 to 25 oC), have actually shown increased height, leaf area and number of flowers during cultivation in

When transferring wild plants to man-made sites for their *ex situ* cultivation, the amount of water to be offered to them remains largely unknown. The watering regime followed is based merely on observations regarding the natural habitat of the species in question or

and continued to grow without apparent problems (Krigas et al., 2010).

**4.2. Cultivation guidelines regarding temperature range**

maintaining temperatures within these limits during cultivation.

may range from 3.9 oC to 29.5 oC (Krigas et al., 2010 and Fig. 2).

comparison to those transplanted outdoors (Krigas et al., 2010).

**4.3. Cultivation guidelines regarding watering regimes**

The precipitation profiles derived from GIS for the target plants may indicate differences regarding the water requirements preferred and/or tolerated by each one of them in the wild. For example, the GIS-derived ecological profile of *A. occulta* published by Grigoriadou et al. (2011) for its *ex situ* conservation, indicated a watering regime equal to a mean total annual precipitation of 744 mm (7–136 mm per month) which was followed for the proper cultivation of the *in vitro* propagated plants.

Seasonal variation of the watering regime is equally important as the total amount of water offered to plants. To illustrate an example, the GIS-derived ecological profiles of *Thymus holosericeous* and *Silene cephallenia* subsp. *cephallenia* may be taken into account (Fig. 2, 3, 5, 6). It becomes evident that during the wettest and driest quarters of the year, *Thymus holosericeus*'s habitat receives from 40 to 504 mm, while that of *Silene cephallenia*  subsp. *cephallenia* from 26 to 476 mm. During autumn, no noticeable differences exist regarding mean monthly precipitation in the natural habitats of these plants. However, from January to August mean monthly precipitation is consistently higher in the intermediate altitude habitats of *Th. holosericeus*, while until autumn *S. cephallenia* subsp. *cephallenia* (which is naturally found close to sea level) receive comparatively lower precipitation (Fig. 5, 6).

**Figure 6.** Precipitation profiles for the original collection sites of *Thymus holosericeus* and *Silene cephallenia* subsp. *cephallenia* derived from their ecological fact sheets.

Hence, the GIS-derived precipitation profiles suggest that the watering need (both in terms of water amount received and frequency of watering) is not the same for these plants (Table 1), although both should be under a rather low water regime (regardless of whether it is winter or summer). *S. cephallenia* subsp. *cephallenia* has comparatively lower water demands both annually and seasonally which should result in a more restricted watering schedule, while *Th. holosericeus* has somewhat intermediate watering needs, although a relatively low water regime seems to be equally suitable since it is considered as a xerophytic species (Krigas et al., 2010).

GIS and *ex situ* Plant Conservation 167

Calculation of different watering regimes for different groups or target

Selection of different growing media (regarding texture, pH, drainage) for different groups or target species

Selection of different growing media and development of fertilization regimes for different groups

Selection of different growing media (regarding texture, drainage) and potting volume for different groups or

Selection of appropriate sites and conditions both in greenhouse and in *ex situ* cultivation sites for different

Selection of temperatures for seed germination and asexual propagation for different groups or target species

groups or target species

Shading and ventilation for temperature regulation

or target species

Calculation or watering regimes, calibration of different watering regimes in different seasons and/or in different months for different groups