REZONING DROUGHT PRONE AREAS, DEVELOPMENT OF LAND SUITABILITY MAPS FOR UNDERUTILISED CROPS IN SOUTH AFRICA
2.0 Status of agriculture in South Africa
2.1 Climate change and land use in South Africa
2.2 Overview of farming and cropping systems in South Africa
2.3 Opportunities for NUS
3.0 Fitting NUS into current landscapes
3.1 Developing land suitability maps How many ways can be used to develop land suitability maps. Land suitability maps in SA (how were these developed and for what crops/activities, time lines and by who using what resources. Critic strengthens and weakness of current suitability maps
3.1.1 Multi-criteria decision analysis – Common methods analytic hierarchy process (AHP), weighted linear combination, ordered weighted averaging, ELECTRE, PROMETHEE, VIKOR and multiple-objective land allocation
3.2 Tools used to develop land suitability maps
3.2.1 Geospatial tools (range of tools and required variables and quality)
3.2.2 Crop simulation models (range of models that have been used and required parameters)
3.2.3 Habitat suitability models (range of models used and required variables)
3.3 Conceptual framework for developing NUS land suitability map
3.4 Recommendations and Conclusion
Coping with climate variability and climate change are major challenges facing smallholder farmers in Southern Africa (Frederick, 2016). There are over layering challenges of climate change in South Africa. A number of meteorologists agree that South African climate is changing with the rate of transformation seemingly faster than what is normally expected (Bryan et al., 2009; Roberts, 2010; Bishaw et al., 2013; Muller and Shackleton, 2014). Within the agriculture sector, an extreme event like the meteorological drought is debatably the most challenging climatic event that confuses farmers and has major negative impacts on rain-fed crop yields (Rosenzweig, et al., 2007;Hall, et al., 2017;(Mabhaudhi, 2012; Holmgren et al., 2006). This has led to recurrent partial to total major crop failure Conway et al., (2008) with consequences to household food insecurity and poor livelihood (Mishra et al., 2008).
According to South African Policy on Food and Nutrition Security (2013) research efforts have identified underutilised crops had potential to reduce food insecurity and improve undernourishment to vulnerable people. A number of sustainable goals (SDGs) 1, 2, 3, 13, 15 are aligned with the promotion of indigenous crops Several underutilized crops have comparative advantage, they are well-adapted to marginal areas and currently attracting considerable attention because of their potential to improve food security (van der Merwe et al., 2016; Mabhaudhi et al., 2017b; Maseko et al., 2017). This makes them suitable crops for promotion in marginal production areas. However, these crops race with common crops for good agricultural land and they can weaken national food security if the suitable productive land is located to underutilize crops. There were many efforts in South Africa to model and development of underutilised crops production guidelines (Spreeth et al., 2004; Slabbert et al., 2004; Mabhaudhi et al., 2013; Chibarabada et al., 2015; Chimonyo et al., 2016; Hadebe et al., 2016).
.Despite the importance of indigenous underutilized crop in South Africa. There is a big gap in trying to quantify the marginal areas (suitable for NUS) and try to early warn the impacts of drought on underutilized crops.Therefore, it is important to develop an early warning system for drought preparedness to quantify underutilised crops and reduce losses, which can be achieved with drought prediction through the improved understanding of drought causes and evolution (Liyan, 2017). In past few decades and researchers used various methods to monitor drought, based on either dynamical or statistical methods (Mishra & Singh, 2011). More emphasis was on major cereal crops but not on underutilised crops.
Drought indices are quantitative measures that characterize drought levels by assimilating data from one or several variables (indicators) such as precipitation and evapotranspiration into a single numerical value (Liyan, et al., 2018). There are number of drought indices which were developed and used to characterise different drought types (Mishra and Singh, 2010; Zargar, et al., 2011; Bachmair, et al., 2016). Amongst the most used spatial and temporal drought indices are Normalized Difference Vegetation Index NDVI, Water requirement satisfaction index (WRSI) and Standardized Precipitation Index (SPI) (Legesse & Suryabhagavan, 2014).The Palmer Drought Severity Index (PDSI) Palmer, (1965) and standardized precipitation index (SPI) have been widely used to monitor drought (McKee et al., 1993). Standardized Precipitation Index (SPI) expresses the actual rainfall (recorded) as a standardized departure with respect to the rainfall probability distribution function and hence the index has gained its applicability in recent years as a potential drought pointer permitting comparisons across space and time (Liyan, et al., 2018). Standardized Precipitation Index (SPI) is a fast drought response index that pinpoints a flash meteorological drought alarm by the integrity of satellite climatic data and biophysical data in a map or graphic format (Shiru, et al., 2018).
The ability of NUS to tolerate water stress and strive in marginal areas, researchers recommended the development land suitability maps and identifying marginal areas where underutilised crops can be grown. In this case, it is important to rezone drought prone areas, modelling crop phenology against ecophysiology data and creating land and crop suitability maps for NUS. There are several researches which recommended to set up the quantity and quality of resources on NUS to assure its future productivity and sustainability of natural resources (Philips et al., 2017; Guiying, et al., 2017; Rabia, 2012). The occurrence of extreme events due to climate change and poor management of the resources can lead to an imbalance in the ecosystem and then causing problems like food insecurity. Therefore, with over layering climate change challenges, population pressure and dwindling of agricultural productive land there is a need of updating land suitability maps or evaluation in South Africa.
Land suitability refers to the ability of a portion of land to tolerate the production of crops in a sustainable way (Malczewski, 2007). In general, land suitability mapping can address the questions “which” and “where”; which land use is to apply under certain conditions and where is the best area to apply this land use(Bera, 2017).The process of land suitability classification is the appraisal and grouping of specific areas of land in terms of their suitability for defined uses (FAO, 1976; Mendas and Delali, 2012). Mapping of land suitability maps are very important to identify the main limiting factors for the agricultural production and enables decision makers, farmers and agricultural support services to develop a crop management able to overcome such constraints, increasing the productivity. In developing land suitability maps, the parameters of great soil group, land use capability class, land use capability sub-class, soil depth, slope, aspect, elevation, erosion degree and other soil properties must be considered(Hopkins, 2007; Li, 2017; Rabia et al., 2013).
In South Africa drought is now a common phenomenon in most of the rainfed agricultural lands in the semi-arid and sub-humid regions. South Africa climate is characterised by erratic rain fall distribution both space and time. Mid-season dry spells are now common, mostly affecting smallholder farmers who practices rainfed crop production(Bryan et al., 2009; Ziervogel et al., 2014). In South Africa several studies showed that underutilised indigenous crops were drought tolerant, had good heat stress tolerance and were adapted to low levels of water use. However, unlimited of information on crop production, water use, and land suitability maps have previously been documented as major challenge in trying to promote NUS (Chivenge et al., 2015a; Chimonyo et al., 2016a; Mabhaudhi et al., 2016; Chibarabada et al., 2017a)
There are several methodologies which can be used to develop crop suitability maps. Multi criteria decision making (MCDM) integrated with geospatial techniques can be used to define potential land use to solve complex problems of land management(Kahraman, 2008; Velasquez and Hester, 2013; Mardani et al., 2015; Kumar et al., 2017).This techniques extensively used for land suitability analysis to identify the potential lands for agriculture management FAO et al., (2007) in agriculture parameters of soil texture, organic matter content, soil depth, slope and land use or land cover (Shalaby et al., 2006; Bandyopadhyay et al., 2009; Jafari and Zaredar, 2010; Cengiz and Akbulak, 2009; Chandio et al., 2011; Feizizadeh and Blaschke, 2012; Akinci et al., 2013; Garcia et al., 2014) and geospatial techniques (Javadian et al., 2011). Geographical information system (GIS) is a useful technique to investigates the multiple geospatial data with precision and higher flexibility in land suitability analysis (Mokarram and Aminzadeh, 2010; Mendas and Delali, 2012). GIS techniques are also used to create various crop suitability maps which can be in an analytical hierarchy process to develop crop suitability model for agricultural purposes (Xu et al., 2012). The integration of RS-GIS, Fuzzy-logic and application of Multi-Criteria Evaluation using Analytical Hierarchy Process (AHP) could provide a superior database and guide map for decision makers considering crop land substitution in order to achieve better agricultural production(Singha and Swain, 2016).Crop land suitability mapping has been identified as a multi-criteria evaluation (MCE) problem (Sys et al., 1991). Determining and assessing the suitability of an area for crop production requires considerable land use accuracy in land mapping.
Many of the aforementioned methodologies are expressed in qualitative terms rather than precise numeric values and hence crisp classification of the land unit into various suitability classes is difficult to achieve. Wang et al. (1990) proposed the use of fuzzy sets in land suitability evaluation, wherein the sharp boundary between the suitable and non-suitable classes was replaced by the concept of degree of truth (membership). Many fuzzy membership-based methodologies have been developed to create land suitability maps, but by considering only the land potential (Wang et al., 1990; Ahamed et al., 2000).
In south Africa the land capacity classification systems were used to create land suitability for agriculture, which groups land using physical properties (soil, terrain and climate) based on their capability to produce conventional food and pasture(Hillyer et al., 2006; Karakus et al., 2015; van Niekerk et al., 2016). There is limited information about crop land suitability for NUS in South Africa. Exclusion of NUS in the classification system has contributed to the promotion of a few major crops neglecting NUS(Mabhaudhi et al., 2017c; Stoeber and Bokelmann, 2018). With the importance of NUS in South Africa, there is need to exploit the potential of existing marginal agricultural lands under smallholder farming systems by creating land suitability maps. Understanding NUS geographic range of adaptability is important with concerns over global climatic changes(Chivenge et al., 2015b; Mabhaudhi et al., 2017c).
The key methods used to study crop suitability in an ecosystem are bioclimatic models, alternatively known as envelope models, climate response surface models, ecological niche models or species distribution models(Mustafa et al., 2011; Rose et al., 2016). Bioclimatic models or species distribution models (SDMs) are widely used for estimating changes in habitat suitability and the accuracy of these models is critical for guiding effective land suitability evaluation management decisions. SDMs match crop phenology into landscape, climate and habitat variables to predict species occurrence probabilities across the landscape(Mustafa et al., 2011; Secondi, 2014). To test the applicability of bioclimatic models in South Africa landscape, crop models are going to be used to validate the suitability of NUS in marginal areas. In a number researches in South Africa, crop models have shown that they can be used to match crop phenology and ecology(Chimonyo et al., 2016b).
The mentioned methods to assess the crop land suitability for agricultural production need to be combined to create crop land suitability in South Africa for NUS.This study aims to develop crop suitability maps for South Africa, by using remote sensing data, GIS tools, crop model and bioclimatic models to develop crop suitability maps for South.
To identify high risk marginal croplands in South Africa that are potential suitable for NUS (Sorghum, cowpea, taro and amaranth)
Marginal areas experiencing low crop yields but high productivity potential for NUS may be suitable for growing NUS, the fact that NUS continue to survive in these marginal agro-ecologies gives the credibility to the argument that NUS have traits that tolerate several abiotic stresses such as drought (water stress) and heat stress.
1.4 Research objectives
1.4.1 To identify meteorological drought prone areas using the aridity index in South Africa
1.4.2 Assessing suitability of underutilised crops (sorghum, cowpea, taro and amaranth) in South Africa
1.4.3 To use crop models to develop cropping guidelines of (taro and amaranth)
1.4.4 To use bioclimatic model to develop species suitability maps of underutilised crops in South Africa
2.0 LITERATURE REVIEW
2.1 Status of agriculture in South Africa
2.2 Climate change and land use in South Africa
The 4 major anthropogenic gases (carbon dioxide, water, methane and nitro oxide) are major drivers of global warming causing global significance of climate change(Barker, 2007; Monks et al., 2009; Piao et al., 2010). The positive and negative impacts of climate change across the globe are still being documented and debated, but the potential changes in precipitation, temperature and sea level rise over the next century are likely to have important impacts on land use like agriculture. In South Africa where agriculture is the mainstay of the economy and the inhabitants (approximately 85 % of the population lives in rural areas) of which mainly rely on rainfed-agriculture (95%) as a primary source of livelihood(Department of Agriculture, 2011; Goldblatt, 2011). The rapidly growing population, desertification, increasing amounts of arable agricultural land are being lost to the spread of urban settlements in this nation, and as a result, food security is gradually becoming a major burden.
As anthropogenic impacts on earth’s surface continue to overlay Niang et al., (2014), the effects of extreme events on future climate are still far from known and negative climate change effects are expected to adversely affect agricultural production. The ongoing changing in global climate and climate variability have become major concerns for food security. Therefore, adaptation of the agricultural sector is very important to protect the livelihoods of the vulnerable and to ensure food security. A better understanding of farmers’ perceptions of climate change and climate variability, ongoing mitigation and adaptation measures and the decision-making process is important to inform policies aimed at promoting successful adaptation strategies for the agricultural sector especially on NUS(Waha et al., 2013; Mabhaudhi et al., 2017b).
As environments become more abiotically stressful (increased extreme events like drought and higher temperatures) suitability of crops tend to be affected by change of climate(Sheffield and Wood, 2008; Cai et al., 2014). Climate change and climate variability are stressful to farmers and crops, because crops response to natural selection that makes changes in environmental conditions a risk to existing crop biodiversity(Gienapp et al., 2008; Frank, 2011). Climate change will largely increase abiotic stress in environments, increasing aridity and heat stress. However, some environments will become
cooler and wetter. Altered rainfall patterns predicted with climate change may shift environments from arid conditions with limited competition to mild conditions where competitive interactions dominate (Choat et al., 2012).
Previous studies have highlighted the importance of cross-sectorial approaches to assess the impacts of climate change on agriculture especially on NUS. For example, (Palmer et al., 2008) pointed out the importance of collaborations among multiple partners and wise land use planning to minimize additional pressure on arable land, stating that special attention should be given to diversifying and replicating habitats of special importance to NUS.
2.3 Overview of farming and cropping systems in South Africa
South Africa’s farming and cropping systems are mainly determined by the agro-ecological zone in which they exist(Johansen et al., 2012; Tibesigwa et al., 2017). The most important natural factors affecting the type of farming suited to any area are rainfall, temperature, topography and soils(Jiang et al., 2009). In addition to these, certain man-made factors, such as vicinity to markets, rail and road development, irrigation facilities and the price structure for the various commodities, may exert an important influence on farming systems. South Africa climate is classified as semi-arid and its water profile is rapidly transforming from water scarce to water stressed. The annual average rainfall varies from 500 mm-600 mm, which is far below the world’s average of 860 mm per annum(Archer et al., 2009; Vetter, 2009; Mitchell, 2013). The total annual rainfall is not much important in terms of crop development, but the rainfall distribution determines the crop productivity. Though 50% of the rainfall is estimated to fall, about 15% rain on the land. In South Africa, about 85% of the country is occupied smallholder farmers and the area are marginal(Mapiye et al., 2009; Thamaga-Chitja and Morojele, 2014; Meijer et al., 2015). Rainfall is unreliable for crop production and it is in the same areas where there are high incidences of food insecurity, malnutrition and poverty.
In South Africa rainfall is undoubtedly the dominating factor determining the system of farming suitable for any area. Several parts of South Africa, the rainfall distribution is poor in space and time. The rainfall, however, may have a comparatively low efficiency for crop production due to higher temperatures, low average humidity and the frequency of torrential downpours which often result in excessive runoff particularly where slopes are steep(Lobell et al., 2008; Dai, 2011; Ziervogel et al., 2014). Characterising South African climate using rainfall and temperature, the country is sub-divided into six agro-ecological zones (AEZs) namely 1. desert, 2.arid,3. semi-arid, 4.sub-humid, 5.humid and 6.super humid(Walker and Schulze, 2008; Philippon et al., 2012). In South about 12% of total land cover is suitable for crop production and of this only 22% has a high arable potential ( SA, Year book, 2008) (figure 1).
Figure 1. Rainfall distribution(Richard and Poccard, 1998) and farming systems in South Africa(Vrieling et al., 2011).
Generally, the amount of rainfall decreases from east to west with super-humid region, which
constitutes 2.8% of the country being situated in the east and arid and desert, which constitutes 47.4%in the west (Figure 1). Based on the AEZs (figure 1,) several smallholder farmers in South Africa are located in desert, arid and semi-arid zone where mid-season dry spells are common. Small holder farmers main farming systems are agro-pastoral, regardless of the AEZ, as livestock (including cattle, horses, donkeys, goats, pigs, sheep, chickens and turkeys) constitute an important cultural role(Mavengahama et al., 2013; Samberg et al., 2016).
In South Africa a guiding principle in the farming systems is that in the more favourable rainfall, the areas are suitable for cereal crop productions and pasture production subservient (it is complementary) to arable production, while under the drier conditions arable should be subservient to pasture. In the medium rainfall areas, the pattern of production, and therefore systems should follow an intermediate course between these two extremes(Carter and May, 1999; Cecchi et al., 2010). Where the effective rainfall is normally very low, production should be based on pasture entirely unless irrigation is available. On areas where the effective rainfall is normally very low NUS are considered adaptable to marginal lands(Lobell et al., 2008; Chivenge et al., 2015a; Mabhaudhi et al., 2017b). Although cultivating NUS in marginal lands and in a changing climate presents huge advantages, parallel hindrances may have elevated major crop species within smallholder farming systems. Several researchers argued that the poor establishment of NUS may partly explain why farmers have neglected to cultivate NUS because they compete with major crops on arable land(Lobell et al., 2008; Bvenura and Afolayan, 2015; Chivenge et al., 2015a; Mabhaudhi et al., 2017b). Therefore, to exploit the full potential of NUS and use them as adaptation strategy of climate change a clear crop (NUS) suitability maps should be created and crop production guidelines must be documented or formulated.
2.4 Opportunities for NUS
Climate change and climate variability negative impacts call for researches on climate resilient crops to determine which crop species will be fit for future climates. The complex interactions of water scarcity associated with climate change and variability and spiral increase of population in South Africa require innovative strategies to address food insecurity, undernourishment and climate adaptation. Climate adaptation, “the process of adjustment to actual or expected climate and its effects”(Hoffmann et al., 2011), is largely designed to lessen the negative impacts of extreme events induced by climate change on human livelihoods and natural recipient (Füssel, 2007; Aitken et al., 2008; IPCC, 2014; Noble et al., 2015).There are number of adaptation and mitigation measures to reduce the negative effects of climate change. An ecosystem-based adaption is important. Ecosystem-based Adaptation (EbA) is a specific type of climate adaptation that “uses the sustainable management, conservation, and restoration of ecosystems to provide services that enable people to adapt to the impacts of climate change” (Vasseur et al., 2017b; a). One of the EbA is growing or promoting of neglected and underutilised species (NUS).
Neglected and underutilised species are plant species that are part of a larger biodiversity, were once popular (in and out of their centres of diversity) but have since been neglected by users and research but however remain important in the regions of their diversity. Historically, such crops were important in food security and nutrition security through providing healthy alternatives when the main crop failed due to extreme events like drought especially in Southern Africa where drought are common and linked to sea surface temperatures ENSO events like EL Nino(Timmermann et al., 1999; Manatsa et al., 2008b; a; Müller and Roeckner, 2008).
In South Africa’s agro–biodiversity, NUS are important (they have genetic diversity that underpins them as well their adaptation to ecological niches) with potential to contribute to meaningful socio-economic development and transformation in poor rural areas. The promotion of NUS will depend on to a large extent on the availability of information describing their agronomy, water-use, possible drought tolerance and the availability of systematic information of potential areas which can be utilised to grow NUS with less competition to major crops(Mapiye et al., 2009; Mabhaudhi, 2012; Chimonyo et al., 2016b; Mabhaudhi et al., 2017a; b, 2018). Unlike most staple crops, NUS are common among sub-Saharan Africa (SSA) farming systems. Several underutilised indigenous crops have been reported to possess attributes that make them suitable for production under low input agricultural systems and in marginal production areas which typify South Africa’s rural landscape (Govender et al., 2013; Mabhaudhi et al., 2017b; c) (Table 1).
Table:1 Selected characteristics of NUS (Chibarabada et al., 2017b)
Cereals Common name Drought tolerance Heat stress tolerance Water use Nitrogen requirement Time to maturity Yield
(mm) (kg ha-1) (Days) (kg ha-1)
Grain Legumes Sorghum Yes Yes 261 – 415 100 – 150 100 – 120 2 802 – 4 304
Pearl millet Yes Yes 166 – 431 40 60 -75 2 670 – 2 765
Finger millet Yes Yes –– 90 – 120 3 400 – 4 700
Tef Yes Yes –– 30 80 – 120 1 500 – 3 400
Root and tubers Bambara nut Yes Yes 300 – 638 50 – 75 120 -150 500 – 2 400
Lablab Yes Yes –– 20 160 – 200 560 – 660
Pigeon pea Yes Yes 331 – 551 120 90 – 270 1 816 – 2 643
Cowpea Yes Yes 133 – 265 30 90 – 150 776 – 1 120
Velvet bean Yes –– 75 100 – 290 1 300 – 2 400
Marama bean Yes –– 1 200 – 2 000
Root and tubers
African Leafy Vegetables Taro Yes 800 – 1 288 400 240 – 300 3 830 – 17 330
Sweet-potato Yes 298 – 478 300 120 – 160 25 497 – 31 020
Cassava Yes Yes 651 – 1 701 60 360 -600 4 550 – 29 680
Yam Yes ––- 240 120 – 220 4 000 – 24 000
African Leafy Vegetables Bottle gourd Yes Yes 154 – 405 150 45 – 120 490 – 4 000
Black jack Yes Yes –– 30 – 60 32 000 – 46 760
Jews Mallow Yes Yes 78 – 258 45 30 – 60 1 700 – 2 400
Spider plant Yes Yes –– 80 6 500 – 38 400
Amaranth Yes Yes 111 – 448 90 20 – 45 3 400 – 5 200
Nightshade Yes Yes –– 150 60 – 90 2 310 – 4 120
Chinese Cabbage Yes X 71 – 286 200 60 – 90 3 400 – 4 900
Wild mustard Yes Yes –– 150 20 – 60
Fitting NUS into current landscapes
Developing land suitability maps
Land use suitability analysis is the process of determining the suitability of a given land area for a certain type of use (agriculture, forest, recreation) and the level of suitability(Saaty, 1980; Singha and Swain, 2016). The process of land suitability classification is the appraisal and grouping of specific areas of land in terms of their suitability for defined uses. This kind of planning land suitability analysis is the starting point for the development and management of natural resources in a sustainable manner. Often the terms “suitability” and “capability” are used interchangeable, capability is the inherent capacity of land to perform at a given level for a general use, some literature defined capability as a classification of land primarily in relation to degradation hazards and while suitability is aligned to adaptability of a given area for a specific kind of land use(Chen et al., 2010; Bera, 2017). Land suitability mapping is important for selection of crops and biodiversity especially in environment and climate is changing at speed more than it was expected.
Land suitability mapping is strategic planning tool and essential tool to for early warning systems, such as Crop Watch, the Early Warning Crop Monitor , Global Information and Early Warning System (GIEWS),and the Famine Early Warning Systems Network (FEWSNET), Forecasting Agricultural output using Space, Agrometeorological and Land-based observations. Such mapping and analysis represent an important step in agricultural production assessment and provides information for environmental climate change studies such as promoting NUS in marginal areas in a changing climate in South Africa(Moeletsi and Walker, 2012; Estes et al., 2013).
In land suitability mapping, a homogeneous area in all aspects of land are called land units(Zonneveld, 1989; Britz et al., 2011). A combination of one land unit and one land utilization type (with one set of land-use requirements) constitutes a land-use system. So, the agricultural land suitability is a function of crop requirements and soil or land characteristics(FAO, 2007; Mustafa et al., 2011). Matching the land characteristics with the crop requirements and phenology gives the suitability. Therefore, suitability is a measure of how well the qualities of a land unit match the requirements of a land use or crop requirements. The process involves the execution and interpretation of basic surveys of climatic (in-situ and space) data, soils, vegetation and other aspects of land in terms of the requirements of alternative forms of land use(Hopkins, 2007; Li, 2017).
There are two land suitability evaluation approaches namely qualitative and quantitative. Qualitative approach is to assess land potential in qualitative terms, such as highly suitable, moderately suitable, or not suitable(Bodaghabadi et al., 2015). While quantitative assessment method of land suitability is given by numeric indicators. Numerical modelling uses mathematical models to describe the physical conditions of geobiophysical scenarios using numbers and equations. In land suitability, the technique is used to tackle complex land suitability problems by computational simulation of scenarios. In land suitability models, climatic data, soil and landscape properties are important input data. The fact the process is complex it is important to combine different methods and tools like Geographic Information Systems (GIS) and Remote sensing (RS). The integration of RS-GIS, Fuzzy-logic and application of Multi-Criteria Evaluation using Analytical Hierarchy Process (AHP) could provide a superior database and guide map for decision makers considering crop land substitution to achieve better agricultural production(Hopkins, 2007; Singha and Swain, 2016). The mentioned methods have strength and weakness as alluded below
The FAO approaches
The FAO approach defines land suitability as aptitude of a given type of land to support a defined use(FAO, 2008). The basic idea underlying the proposed method of land suitability classification is that the land should be rated only on its value for a specific purpose. The FAO has five different classes, ranging from “Unsuitable” to “Highly suitable”, whose codes are constituted by a capital letter (indicating the order) and a number (indicating the class); identify the land suitability fora certain purpose (Table 2)(Fontes et al., 2009).
Table 2: Suitability indices for the different suitability classes(FAO, 2007).
Suitability Class Suitability index (SI) Description Class
S1 Highly suitable>80
Land having no limitations for a given use, or limitations that do not reduce appreciably
the productivity and benefits, with no need for a high level of input
S2 Moderately suitable 60-80
Land having minor limitations that could reduce productivity or benefits, additive
inputs are required to reach the same yield as that of class S1
Marginally suitable 45-59
Land having moderate limitations for a certain use, in which the amount of surplus
input is only marginally justified
N1 Currently unsuitable 30-44
Land with severe limitations for the land use under consideration. Every sustainable use is precluded at the present time and the costs for correction are unacceptable with
the existing condition. Only new technologies could improve land productivity
N2 Permanently unsuitable