The study area falls within Ghana's Forest-Savannah Transition Zone (FSTZ). The geographic location lies between longitude 1° 10′ & 1° 55′ West and latitude 7° 20′ & 7° 55′ North, covering ~2,592.09 km² (see Figure 1).

Soil moisture and temperature regime of Nkoranza's north and south districts is udic and hyperthermic (Wambeke, 1974). According to the Koppen-Geiger climate classification, the area experiences the tropical savannah climatic regime (Aw) (Peel et al., 2007). The study area has distinct climates (McSweeney et al., 2010). Annual rainfall, although high, varies between 1,200 and 1,600 mm year⁻¹ (Table 1). Temperatures are high throughout the year (Kasei, 1993; AQUASTAT and FAO, 2005), with a mean monthly temperature range between 24 and 30°C (Dickson and Benneh, 1995). Annual potential evaporation is about 1400 mm and relative humidity varies from 90 to 95% in the rainy season to 75 to 80% in the dry season (Christiansen and Awadzi, 2008). The study area is a major producer of maize (Dickson and Benneh, 1995).

The FSTZ is characterized by deep, well-drained soils with sandy clay loam texture, strongly weathered, highly leached and acidic (pH: 5.1–6.5) (Adjei-Gyapong and Asiamah, 2002). These soils are easily eroded once the forest or savannah vegetation has been removed [Food and Agriculture Organization (FAO) of the United Nations, 2015, 2020a]. The soils contain a small amount of plant-available nutrients, particularly nitrogen (N), phosphorus (P), and potassium (K), with low cation exchange capacity (<10 cmol kg⁻¹). Drainage is related to topography. Poorly drained, flood-prone clay to loamy sand textured gleysols are found in valley bottoms (Annan-Afful et al., 2004, 2005; Owusu-Bennoah et al., 2008). Six (6) soil types (Table 1), namely: Kpelesawgu (Dystric Plinthosol), Changnalili (Gleyic Plinthosol), Damongo (Rhodic Luvisol), Murugu (Haplic Luvisol), Bediesi (Dystric Nitisol), and Sutawa (Thapto-Plinthic Luvisol) was identified and classified using the Ghana Interim Soil Classification System (GISCS) (Phillips and Wills, 1963; Adjei-Gyapong and Asiamah, 2002). The soils were reclassified using the FAO/WRB classification [IUSS Working Group (WRB), 2015]. Most of these soils were developed from hydrological conditions along slopes. These geomorphological processes resulted in different soils from uplands (north-west and south-west) to lowlands (north-east and south-east sections of the Nkoranza district), resulting in various formations of soil associations. Bediesi-Sutawa association was associated with upland soils, Damongo-Murugu was attributed to middle slope soils, while Kpelesawgu-changnalili association were ascribed to lowland soils (Adu and Mensah-Ansah, 1995; Agyili, 2003) Soils of the district are generally deep on the upper, middle, and lower slopes.

A systematic stratified sampling procedure was used to distribute sampling points throughout the study area under careful consideration of topography and the spatial pattern of smallholder land use systems. For morphological description of benchmark soils, soil pits (2 m L × 1.5 m W × 1 m D) were dug and described according to the recommendations of IUSS Working Group (WRB) (2022). Sixty (60) soil samples were collected at a depth of 0 – 20 cm, air-dried and passed through a 2-mm sieve. Soil color was determined using the Munsell Color Chart. Soil particle size (sand, silt, and clay) was determined with standard Bouyoucos hydrometer method (Bouyoucos, 1962). Bulk density (BD g cm⁻³) was determined using the core method (Blake and Hartage, 1986). The soil reaction (pH) and electrical conductivity (EC) were measured in distilled water in a soil: water ratio of 1:2.5 w/v soil/water suspension (Olsen et al., 1982; Thomas et al., 1996; Rhoades, 2018). Total nitrogen (TN) was determined using the Kjeldahl method (Soil Survey Staff, 2006), available phosphorus (Av. P) determined colorimetrically after extraction with Bray's No.1 solution (Bray and Kurtz, 1945), available potassium (Av. K) by flame photometry (Motsara, 2015), and cation exchange capacity (CEC) was determined by ethylenediamine tetraacetic acid (EDTA) titrimetric method after extraction with 1 M ammonium acetate solution buffered at pH 7 (Thomas, 1983). Soil organic carbon (SOC) was determined with the Walkley and Black wet combustion method as described by Jackson (1973). Base saturation (BS) was determined as the ratio of basic cations in CEC.

In terms of agriculture, land suitability evaluates land to meet the agro-ecological requirements of a given crop to increase yield (Kawy and El-Magd, 2012). Land suitability assessment assesses relevant land characteristics such as soil, climate, and topography [Food and Agriculture Organization (FAO) of the United Nations, 1976; Young, 1980] to the requirements of a particular crop. These were regrouped into three thematic indicators. These indicators, as adopted from Baroudy (2016), and Diallo et al. (2016) are soil fertility, chemical, and physical quality indices (Equation 1):

The above equation was used to calculate land suitability for maize in the study area. From equation 1, LS is the land suitability factor, FQI is the soil fertility quality index, CQI is the soil chemical quality index, and PQI is the soil physical quality index. Soil fertility index (Equation 2) was calculated as:

where S_SOC, S_TN, S_Av.P, S_K, and S_CEC are parameters that express factors for soil organic carbon, total nitrogen, phosphorus, potassium, and cation exchange capacity content. The chemical quality index was calculated using Equation 3;

where S_Na, S_pH, and S_EC are parameters that express factors for sodium, soil pH, and electrical conductivity. The physical quality index was calculated using Equation 4

where S_t, S_s, S_cf, S_d, S_w, and S_g are parameters that express factors for texture, structure, coarse fragment, depth, drainage, and slope (gradient). The parameters or factors for the evaluation process were rated (Sys, 1985; Sys et al., 1993). For this evaluation exercise, rates were assigned to the elements of each particular parameter with valid scores ranging from 0, the worst condition, to 100, rated as the best condition (Baroudy, 2016). For each class, a weighted index score was calculated according to the importance of its role in the land evaluation process. The suitability ratings were as follows: S1-highly suitable, S2-moderately suitable, S3-marginally suitable, and, N-unsuitable. The values of the final results were compared with two parametric methods, the square root and the Storie method (Storie et al., 1976).

For the non-parametric approach, soils were placed in suitability classes by matching these climatic characteristics (rainfall, temperature, humidity) with the agronomic requirements of the maize crop (chemical elements: SOC, TN, SOM, Av. P, Ex. K, etc.). An optimal soil property value for a crop must have no limitation. However, if the attribute is unfavorable to the crop, there is a limitation. These limitations range from 0 (no limitation) to 4 (very severe limitation). A value of 100 is assigned when the characteristics of the soil are optimal for the intended use. However, if the characteristic is not helpful, a low value is assigned, reflecting the degree of limitation for climate (c), topography (t), soil water conditions (w), soil physical characteristics (s) and soil chemical characteristics (f). The product of these parametric values gives the soil suitability index as described in Food and Agriculture Organization (FAO) of the United Nations (1976), Ogunkunle (1993). The Storie method was used in the calculation of the land index (I) (equation 5):

where I is the suitability index. A is the rating of the surface texture parameter, and B, C, and D are the rating values for other parameters. A score ranging from 0 to 100% was determined for each factor and the scores were multiplied to generate an index rating (Storie et al., 1976). The square root method uses the formula (Equation 6):

where I is the square root index, R_min is the minimum rating, and A, B, C, and D are the remaining rating values (Khiddir, 1986).

The rating of each criterion is derived from field observation and laboratory analysis of each land characteristic, and the comparison of these measures with a specific crop (maize) requirement (Table 2). After rating the measurements with threshold values, a rating of 0–100 is assigned to each criterion. Also, a score of the land unit is given a rate of 0–100 by calculation as described by the three methods discussed above. The method of maximum limitation involves the selection of the most restricting rating and/or considering it as the total score for a land unit. Soil series were first classified for their suitability by matching their characteristics with the FAO requirements in Table 2. The suitability class of the soil was indicated by its most limiting characteristics. For the parametric method, each limiting characteristic was rated.

Furthermore, the productivity index for each soil series (pedon) was calculated using Equation 1 above. In all, five (5) land quality factors, thus climate (c), topography (t), soil physical properties (s), wetness (w), and fertility (f), were used for the assessment. Only a member of each group was used to calculate the overall suitability index because there was a strong correlation among variables of the same group (e.g., texture and structure) (Ogunkunle, 1993). All the lowest characteristics for rating each land quality group were substituted into the suitability equation for the actual suitability index calculation formulae. Regarding the potential suitability index, the observed corrective limitation was no longer a constraint. Finally, the suitability indices were assigned to land suitability classes following the Sys ratings (Sys, 1985; Sys et al., 1991, 1993) (Tables 3, 4).

The Munsell Color Chart, a standard system for soil color description was used. The Munsell color notations are systematic, numerical and letter designations of three parameters (hue, value and chroma). A small piece of soil was compared with the standard color chips in the soil color book. Each color chip was described by three components: hue, value and chroma. Thus, hue represents the dominant spectral color which refers to the redness or yellowness in the soil. The value refers to the relative lightness or darkness of a color (amount of reflected light), a value of zero (0) denotes black. Chroma signifies the purity of the dominant color (strength of the color), and a chroma of zero (0) being neutral gray. For example, the notation of 2.5 YR5/6 means a hue of 2.5 YR, value of 5 and a chroma of 6. The equivalent soil color name is “red.”

In order to quantify SI, six (6) land and soil characteristics indicating the resilience and performance of land were used. For each land unit the measured values for the indicators were derived from the field and analyzed according to defined threshold values (Tables 5, 6). By summation of all scores, a minimum value of 6 and a maximum value of 20 (four points for SOC, Clay + Silt content, and three points each for pH, CEC, soil depth, and slope) could be attributed to a land unit. The total points were grouped into four different categories of SI potential. The land with the lowest quality has only a final score between 6 and 10 (category 1). This implies that the soil has intrinsic properties which cannot support sustainable intensification. Land in category 2 can show medium or good conditions (score > 10), but one or even more indicators are in a poor condition. Therefore, intensification is possible only at a high risk. A score between 11 and 15 represents medium (category 3). This implies a poor potential for SI. This means that intensification should be done with much caution. For category 4 a total score between 16 and 20, represents soils which can compensate environmental impacts through agricultural production. This implies that soils of this nature can be recommended for intensive agriculture under the precondition that it can be managed sustainably.

All statistical analyses were performed with Excel and SPSS version 20.

Moist soil surface color differed among soil types. The average moist color reading ranged between 2.5 YR 3/4 (Bediesi series) to 10YR 6/3 (Changnalili series) for the A horizon (Table 7). For B horizon, soil color ranged from 2.5YR 4/6 in Bediesi and Damongo series to 10 YR 5/6 in Kpelesawgu series (Table 7).

The soils are deep (>100 cm), well-drained and gravel-free. The results from textural analysis indicate that these soils ranged from sandy loam to sandy clay loam (Table 8). The percentage of sand varied between 60% (Changnalili series) and 81% (Damongo series) for 0 to 20 cm. For percentage silt, it ranged between 15.2% (Damongo series) and 36% (Changnalili series) for the 0 to 20 cm. Also, the percentage of clay varied between 2.5% (Murugu series) and 6% (Sutawa series).

Chemically, soil pH ranged from 5.20 to 6.60 (Table 9). The percentage of SOC ranged from 0.55% to 2.02%. The concentration of Ex. K ranged from 0.05 to 1.41 to 86.06 mg kg⁻¹. For Ex. K, Sutawa series had the highest (1.41 cmol₍₊₎ kg⁻¹), followed by Bediesi (0.30 cmol₍₊₎ kg⁻¹), Kpelesawgu (0.19 cmol₍₊₎ kg⁻¹) and Murugu (0.15 cmol₍₊₎ kg⁻¹). However, Changnalili and Damongo series recorded the lowest (0.05}. The concentration of available phosphorus (Av. P) ranged from 2.18 to 12.32 mg kg⁻¹. Within soil series, Av. P followed the order: Bediesi (12.32 mg kg⁻¹), Kpelesawgu (7.65 mg kg⁻¹), Sutawa (6.79 mg kg⁻¹), Changnalili (6.50 mgkg⁻¹), Murugu (3.12 mg kg⁻¹), and Damongo series (2.18 mg kg⁻¹). Base saturation ranged from 67.0 and 98.89%. For soil types, base saturation followed the order: Sutawa > Damongo > Bediesi > Murugu > Kpelesawgu > Changnalili series (Table 9). Also, CEC ranged from 3.63 to 12.63 cmol₍₊₎ kg⁻¹. Micronutrient cations (Ca²⁺, Mg²⁺, Na⁺, and K⁺) varied in all soil series. Bediesi series had the highest concentration of these cations. Also, we observed that cation exchange capacity varied between 3.61 cmol₍₊₎ kg⁻¹ in Changnalili series and 12.63 cmol₍₊₎ kg⁻¹ in Bediesi series. Within the soil series, mean values recorded were in the order: Bediesi > Sutawa > Kpelesawgu > Damongo > Murugu > Changnalili series. These results stressed a high potential for soil nutrient leaching in all soil series except Bediesi series. With an EC of > 4 dS m⁻¹ (Table 9), all soils encountered had negligible effects of salinity.

Pearson correlation values >0.70 between physico-chemical properties indicate that most of the indicators positively correlated with each other (Table 10). Organic carbon positively correlated with TN (0.94^**). Also, CEC positively correlated with pH (0.74^**), Ex. Ca (0.96^**), Ex. Mg (0.89^**), ECEC (0.99^**) and negatively correlated with Ex. Acidity (−0.73^**). With respect to percentage BS, it correlated with pH (0.93^**), Ex. Ca (0.82^**), Ex. Mg (0.79^**), CEC (0.85^**), ECEC (0.82^**) and negatively correlated with Ex. Acidity (−0.90^**). Sand recorded a negative correlation between Silt (−0.92^**) and Clay (−0.72^**). From the above, soil physico-chemical processes tend to function simultaneously. However, the trends are broad and high with a correlation coefficient ranging between 0.01 and 0.99 and −0.02 and −0.92 (Table 10).

The rating of land and/or soil characteristics with the requirements of maize (Tables 2, 7) produced suitability classes for maize (Table 11). Most of the soil series encountered were marginally suitable (S3) for parametric and non-parametric methods (Tables 12, 13). The major limitations observed were related to soil texture and structure. These two physical properties of the soil directly affect water-holding capacity, permeability, and other physical properties for the non-parametric (potential) rating (Table 13). Other soil limiting factors were drainage, soil fertility (as measured by CEC, soil organic carbon, organic matter, and total nitrogen content) for the non-parametric (actual) rating.

Soil resilience was calculated for bench mark soils (Bediesi, Sutawa, Kpelesawgu, Changnalili, Damongo and Murugu series) of the study area. The summation of all indicator scores resulted in a score ranging between 12 and 15 representing a moderate SI potential (category 3; Tables 14, 15). This means the soil resources of the study area possess a low (poor) potential for SI thus, intensification could be done with caution. The most limiting soil indicators were SOC and CEC and this could be managed sustainably to increase SI in the study area. From our study, the soils of Nkoranza (north and south) can be recommended for intensive agriculture (SI) under the precondition that it can be managed sustainably.

From Table 16, parametric potential index (PPI), and parametric actual index (PPI) were significant and strongly correlated (r = 0.94, p < 0.003). This indicates that as soil suitability (parametric potential index) increases, soil quality and/or health improves. Also, soil resilience index (SRI) and the parametric potential index (PAI) had a significantly high correlation (r = 0.76, p < 0.037, p being < 0.05), and this means that as soil resilience increases, soil suitability in terms of productivity (yield) for maize increases. This shows that the relationship between PPI and PAI, as well as SRI and PAI are a perfect correlation (Table 16). Hence, it can be deduced that PAI affects the extent of PPI and SRI. However, an insignificant moderate correlation was observed between soil resilience and parametric potential index (r = 0.59, p < 0.104, p being > 0.05).

Soil color readings differed among soil management zones due to cropping and land-use systems practiced by smallholder farmers. Soil color was influenced by the mineralogy and chemical composition of the soil. The presence of manganese oxides imparted the black color to the soil. Also, organic matter in the soil imparted dark brown to black color to the soils sampled. From the above, observed soil colors may have contributed to low organic matter content (Table 7). The abundance of oxidized Fe in highly weathered tropical soils (Young, 1980), may have resulted in the low organic matter. The high Fe-MnO₂ concretions may have accounted for the red to reddish-brown colors in Bediesi series. Several research findings observed higher chroma values of red and associated these soils with low fertility (Desbiez et al., 2004; Laekemariam et al., 2016; Laekemariam and Kibret, 2020). The presence of iron compounds imparted the red, and/or brown color. The red or brown color is mostly related to the extent of oxidation, hydration and diffusion of iron oxides in the soil medium (Foth, 1990).

Most farmers consider dark soil colors fertile compared to reddish hues in the Savannah ecological zone (Desbiez et al., 2004; FARM-Africa, 2005; Haileslassie et al., 2006). The results of this study indicated that soil color is influenced by SOM content. The dark color in the A-horizon decreased with depth. Soils found on slopes not saturated with water had reddish and brownish subsoil colors. Thus, soils of this nature are well-drained and aerated. Soils sampled at poorly drained locations had gray-colored B-horizons (Foth, 1990). Erosion often removed the topsoil layer from the shoulder/back slope of high slopes. This left behind thin and light-colored soils with low organic matter compared to soils of foot-slopes or toe-slopes. High organic matter content was associated with thick A-horizons (Table 7) as observed by Mulugeta and Sheleme (2011) in southern Ethiopia.

Soil pH ranges from (slightly acidic) to (neutral) for Kpelesawgu and Damongo series. According to Food and Agriculture Organization (FAO) of the United Nations (1985) and Diallo et al. (2016), this range mainly corresponds with productive soils in the humid tropics. Achieving optimal production of maize requires a moderate acidic to slightly basic soil with a pH range of 5.5–7.50 [Food and Agriculture Organization (FAO) of the United Nations, 1976, 1985] and well-distributed rainfall (800–1,200 mm) throughout its growing season. However, although the physical conditions and environmental factors are favorable for maize production, SOM can be improved with fertilizers and other organic resources (e.g., poultry manure). Based on expert opinion, the selected soil properties (Tables 1, 2, 5) (Mandal et al., 2017) can be improved. Increasing the suitability of soils can improve maize yield in the short and long term in the study area. Soil pH affects soil biological activity, and the availability of nitrogen to plants can also serve as an essential chemical indicator for soil fertility (Diallo et al., 2016). Soil pH influences plant growth and increases the bioavailability of soil nutrients and the activity of soil microbes (Diallo et al., 2016).

The CEC levels were in the order: Bediesi > Sutawa > Kpelesawgu > Damongo > Murugu > Changnalili series. Bediesi series had a higher CEC compared to the other soil types due to high surface organic matter. Thus, CEC of a soil is controlled by organic matter and clay content. A CEC of <12 cmol₍₊₎ kg⁻¹ is low due to the low availability of organic matter. The variability of CEC within soil series explains the differences in the ability of these soils to hold positively charged ions affecting the stability of soil structure, nutrient availability, soil pH, and the response of these soils to fertilizer when applied (Crewett and Korf, 2008). This finding is in line with McAlister et al. (1998), who stressed that CEC varies with a change in percentage of clay, type of clay, pH, and the amount of SOM among soil types. Base saturation followed the order: Sutawa > Damongo > Bediesi > Murugu > Kpelesawgu > Changnalili series. Soil pH is a major factor in determining the percentage of base saturation of soils. According to Brady et al. (2008), soils of the tropics have a variable charge system, and this charge on the exchange complex gives the soil the strength to attract positively charged basic ions.

Therefore, the higher the soil pH (8.5), the higher the negative charges created and the higher the basic cations absorbed by the soil. The more cations absorbed, the higher percentage of base saturation. Damongo, Sutawa, and Bediesi series had a relatively higher pH than Murugu, Changnalili, and Kpelesawgu series. This could be due to leaching of basic cations in the latter, giving rise to a relatively lower soil pH resulting in a lower base saturation in Changnalili series. Damongo, Sutawa, and Bediesi series had enough vegetation cover to check the leaching of basic cations, and this in the long-term increased soil pH and base saturation. The concentration of soil organic matter and exchangeable cations was low, indicating that the soils are inherently low in fertility. This implies that soil pH influences the efficiency of plant growth. Soil organic matter and exchangeable cations represent actual and/or potential soil fertility (PSF) (Diallo et al., 2016). When soil is mismanaged, it loses its fertility after years of cultivation. In a similar research, Lal (1996) observed a rapid decline in SOM after intensive cultivation. The low soil fertility could be attributed to a decrease in soil pH due to absorption of nutrients by plants and leaching of basic cations beyond plant roots.

Land suitability assessment focus on crop requirements, soil type, and landscape attributes that influence cultivation. For the parametric evaluation process (potential) for maize, the soils of the study site showed moderate suitability for maize production (Tables 11–13). However, except Bediesi series, all other pedons were rated severe for the actual parametric evaluation. The limitation can be corrected (see Tables 12, 13). The parametric and non-parametric methods classified land units as S2 (highly suitable) and S3 (marginally suitable). The chemical characteristics of the soils were the dominant limiting factors that affected the suitability of land for maize farming in the Nkoranza district.

Concerning pedogenesis and the addition of organic inputs, organic matter is low (Wang et al., 2014). As a result, these soils hardly store organic matter in the form of organic carbon. Also, inappropriate farming practices depleted soil nutrients in the identified soil series. Therefore, application of poultry manure which is abundant in Nkoranza district can be used to restore soil nutrient stocks, and improve soil quality (Yimer et al., 2007). From the above discussion, the main limitation (see Table 13) is soil nutrient deficiency, not climate or topography, because the water need for maize is not greater than the edaphic conditions of the surrounding environment (Diallo et al., 2016). With the significant limitation identified as chemical degradation, these soils would be highly suitable for maize production, if much attention is paid to soil management. Also, these smallholders can use poultry manure from the vast poultry industry in the Nkoranza district to enrich soil fertility on their farms. The use of integrated soil fertility management practices can restore the chemical limitation of these soils.

Table 11 indicates land suitability evaluation for maize cultivation. Potential soil fertility based on cation exchange capacity, base saturation, organic carbon, and soil pH indicated that these soils have a limitation. The soils in Tables 7, 9 had different levels of fertility limitations due to different parent materials. Sandstone had more fertile soils and was richer in plant nutrients than those found in old sediments due to abundant weatherable primary minerals in these soils. These physical properties influence soil fertility by controlling the release and supply of nutrients. Soil physico-chemical parameters, as influenced by parent material, directly influence soil fertility for maize production in the transition zone of Ghana. Maize growth and development depend on the potential of the soil parent material to supply nutrients. Climate is not a significant limitation to maize production because mean annual maximum temperature promotes favorable soil moisture conditions for plant growth and development. As a result, good soil management techniques are required to increase soils suitability ratings for maize production. Water is important during the growth and development stages of maize because a deficit or excess can result in a low yield. Several studies indicated that poor drainage reduces maize yield by affecting net photosynthesis, stomata conductance, and transpiration. Areas with high clay content can be managed by establishing drainage systems that remove excess water to promote maize root development.

Soil physical characteristics had no significant limitations except for sites with very high clay content. Maize grows well in soils with sandy clay loam texture with an optimal water table depth (50–60 cm). The low values of Ex. K in these soils are due to low CEC and high amounts of rainfall that promote the leaching of basic cations. Also, the farmers are smallholders who engage in crop production in a fragile environment with little or no resources for good agronomic practices. In recent years, soil degradation through inappropriate farming practices such as uncontrolled grazing and bush burning with low fertilizer use has increased. Appropriate soil fertility management for K fertilization is required to improve yield. Potassium is the most important nutritional factor that determines maize yield because K plays a major role in the proper functioning of the stomata and prevents droughty conditions. Also, its availability promotes the transportation of assimilates from photosynthesis, assists in enzyme activation, and makes the maize plant disease resistant. Soil fertility evaluation results indicated a low Mg:K ratio due to the relatively low Ex. Mg and a high Ex. K values. This means excess K ions depress the uptake of other cations such as Mg²⁺ ions. Low P availability was a major constraint to maize production in the study area. The application of phosphorus and magnesium amendments could increase maize yield. Understanding the nature and variability of soil properties with respect to soil fertility and maize are critical issues to consider in soils formed on sandstone and clay shale parent materials in the transition zone of Ghana.

This study reveals how soil can be included in discussions on where to find land to produce more food to feed the growing population with the adoption of soil resilience principles (Schiefer et al., 2016). The maize fields sampled had soils with moderate soil resilience (Table 15). The soils intrinsic characteristics are favorable for intensification if and only the main limiting factor thus SOC and CEC are managed carefully to avoid causing low soil resilience. The relationship between CEC and other indicators (pH, Ex. Ca, Ex. Mg, and Ex Acidity) as well as between SOC and Sand, Silt and Clay confirms the fact that these physico-chemical properties function simultaneously (Table 10) and at a moderate capacity (Tables 14, 15). Thus, CEC is the soils capacity to retain organic and inorganic positively charged compounds in the soil medium.

Inappropriate land use management has resulted in the degradation of soils hence rejuvenating the negative effects of poor parent material, terrain characteristics and in a changing climate has affected soil resilience (Lal, 1997). The processes of soil resilience involve the mechanisms that influence the soils' ability and rate to recover after disturbance. Good soil management results in the provision of other ecosystem services such as biodiversity, water storage, carbon storage, flood and/or drought regulation etc. According to van Ittersum et al. (2013), the evaluation of the potential of land for SI is important because this could serve as a guide to evaluate yield gap potential including the differences associated with climatic conditions and cropping systems on farmer's fields locally and regionally. This can assist in the prediction of the future potential of sustainable intensification of smallholder agriculture in Ghana.

From the above discussion, the integration of organic and inorganic resources into soil fertility management options (compost, manure, plant residue, etc.) and organic residues can be use by farmers (Bationo et al., 2007; Bekunda et al., 2010) during the beginning of the major and minor farming seasons to increase maize yield from the current 1.75 t/ha in the Nkoranza (North and South District). Also, site specific fertilizer application (Tetteh et al., 2017, 2018) compared to the blanket fertilizer application [mostly two (2) bags of 50 kg NPK and 1 bag of 50 kg of Urea or Sulfate of Ammonia per ha] is recommended for small farm holders in the Nkoranza (north and south) districts. Thus, the relevance of mineral fertilizer use in the intensification of food production on small farms cannot be under estimated especially in areas with soils deficient in nutrients.

According to Tetteh et al. (2018), combined application of organic fertilizer (poultry manure at 2.5 T ha⁻¹) with 60 kg N ha⁻¹ of mineral fertilizer yielded the same yield as the application of sole 90 kg N ha⁻¹. Hence, the application of 90 kg N ha⁻¹ yielded optimum economic returns in the Forest-Savannah Transition Zone (FSTZ). Thus, the current fertilizer recommendation for maize with respect to NPK recommendation is N-P₂O₅-K₂O:90-60-60+1.7 Zn and for recommended blends per hectare is N-P₂O₅-K₂O:15-20-20+0.7 Zn (8 bags/ha + 2bags/ha urea) for the FSTZ of Ghana. Also, the recommended fertilizer formulae blend is N-P₂O₅-K₂O:15-20-20 +0.7 Zn (6 bags/ha + 2 bags/ha urea) (Tetteh et al., 2017, 2018). The mean yield from field validations of the new fertilizer recommendations using the new blends or formulae yielded a mean of 6.0 t/ha. Thus, on a fertile soil with recommended fertilizer application and planting at the onset of the major and minor rainy season, a grain yield ranging between 5 to 8t/ha with high returns is expected compared to the current yield of 1.75 t/ha for Nkoranza (North and South) district and 1.6t/ha of national average yield for Ghana. This would assist improve the efficiency and profitability of fertilizer use on smallholder maize farms in the Nkoranza (north and south) districts in the Forest Transition Agro-ecological Zone of Ghana.

The creation of an enabling environment through the formulation of policies and institutions to facilitate the intensification process in smallholder agricultural production systems should be a national priority. Also, investments into agricultural research as documented in the Maputo declaration that at least 10% of national budgetary allocations should be invested in agriculture and rural development (African Union, 2003) could strengthen innovative systems that increase productivity on small farms. These investments should include the provision of credit (loans, subsidies etc.), input (fertilizers, herbicides, and pesticides) and the creation of markets for agricultural produce. These according to Schut et al. (2016) could address 70% of the constraints associated with the sustainable intensification process at the local, regional and national level in Ghana.