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Sap F 29 Evaluation Essay

INTRODUCTION

At Nigeria’s independence in 1960, agriculture was the mainstay of the Nigerian economy. According to Ilugbuhi (1968), peasant agricultural production for export provided the stimulus to Nigeria’s overall economic growth. Agriculture provided employment to over 75% of the population and accounted for over 70% of total food consumption. It also provided raw materials for industry, export earnings to finance imports and foreign exchange (Alamu, 1981).

The importance of the cotton crop to the Nigerian economy cannot be over-emphasised (Chikwendu, 1993; Adeniji et al., 2007). The lint removed from the seed is used as raw material for the textile industry. The cotton-seed provide edible vegetable oil for human consumption (Kudi et al., 2007). The cotton-seed cake is used as an important raw material for livestock feeds (Barje et al., 2008; Udo and Umoren, 2011). About 80% of total cotton production in Nigeria was carried out by peasant farmers (Adeniji, 2007). The Nigerian textile industry was the second leading employer of labour after the public sector (Idem, 1999; Manyong et al., 2005). Cotton was thus an important source of food for man, feed for animals, raw material for the textile industry, direct employment to cotton farmers as well as an indirect source of employment to workers employed by agro-based industries that relied on cotton as raw material i.e., textiles, edible oil and animal feed manufacturers.

About 21 years after Independence, Abdullahi (1981) observed: Nigeria can no-longer produce enough food for its fast growing population neither could the agricultural system cope with the increasing demands of the agricultural raw materials to keep the country’s oilmills, textile and other agro-based industries operating at full capacity let alone have surplusses for export. In fact many of the agro-based industries which once depended on locally produced raw materials are closing down unless of course they are allowed to import part or all of these raw materials from abroad. Numerous other parameters point to the obvious and undeniable fact that the country is progressively becoming unable to cope with the overall needs of its food and raw materials.

Several reasons were advanced for the decline in the performance of the Nigerian agricultural sector, prominent among which is the increased foreign exchange earnings from the export of crude oil between 1972 and 1980 that led to the neglect of the Nigerian agricultural sector (Asiabaka and Owens, 2002; Walkenhorst, 2007; Sekumade, 2009).

The international oil market plunged in 1982, reducing significantly Nigeria’s ability to finance imports, including food and persistent current account deficits began to emerge while unpaid trade bills began to accumulate (Osuntogun et al., 1997). Signs of economic decline: trade deficits, budget deficits, inflation and balance of payments problems became seriously manifest (Osaghae, 1995). Experts pointed at structural adjustment as the panacea to Nigeria’s persistent economic doldrums. The key argument of the structural adjustment framework for economic policy reform was that state and state interventionism were the sources of economic distortions to African economies since independence (Colclough and Manor, 1991; Olukoshi, 2004).

Structural adjustment, according to Ahmed and Lipton (1997) is: A set of measures that seek to permit renewed, or accelerated, economic development by correcting structural disequilibrium in the foreign and public balances. Often, such measures are required as conditions for receiving World Bank and IMF loans. These reforms attempt to eliminate distortions such as an overvalued exchange rate, high fiscal deficits and restrictions on trade and inefficient public services that often prevent an efficient allocation of resources in the economy.

The broad objective of Nigeria's SAP was to restructure and diversify the productive base of the economy in such a way as to reduce dependency on the oil sector and imports. One of the key policy strategies designed to achieve the Nigeria's SAP goals was the adoption of a market-determined exchange rate (Moser et al., 1997). This is based on the argument in literature that overvalued exchange rates makes domestic products, including agricultural products, not only less competitive with imports but also less profitable as export (Mamingi, 1997). Exchange rate depreciation lowers the foreign currency price of exports and tends to increase the quantity of exports and export revenue in domestic currency (Fang et al., 2005; Hadiwibowo, 2010; Azgun, 2011). Empirical studies by Bahmani-Oskooee and Kara (2003) and Abolagba et al. (2010) reported that currency devaluation increases exports.

Another side of Exchange Rate deregulation is its effect on a country’s Real Exchange Rate (RER). A crucial component in evaluating a country’s macroeconomic performance and the sustainability of its policies is competitiveness assessment, which routinely starts from an assessment of the RER level (Di Bella et al., 2007). Many developing countries adopted real exchange rate devaluation as an effective strategy to boosts exports (Haddad and Pancaro, 2010). Hausmann et al. (2005) and Adeniyi et al. (2011) reported a significant relationship between RER depreciation and rapid economic growth. Real overvaluation hampers exports with a consequent decline in economic growth (Easterly, 2005; Johnson et al., 2007). According to Okonjo-Iweala and Osafo-Kwaako (2007), volatile fiscal spending contributes to real exchange rate volatility. Barnett and Ossowski (2002), domestic currency appreciation and reduction in competitiveness of the non-oil sectors of the economy are likely consequences of fiscal expansions funded by oil revenue. There is considerable theoretical and empirical evidence on the adverse effects of volatility for growth (Fatas and Mihov, 2003; Serven, 2003; Bleaney and Greenaway, 2001).

The main objective of this paper is to assess the impact of exchange rate deregulation and SAP on cotton production in Nigeria. Specifically the study seeks to:

Determine relationship between the Real Exchange Rate (RER) of the Naira and the average cross exchage of the Naira to the US$ over the study period (1973-2007)
Investigate the effects of exchange rates of the Naira to the US$ on the production of cotton in Nigeria (1973-2007)
Assess the impact of SAP on the production and utilization of cotton in Nigeria (1973-2007)

To achieve the objectives of this study, the following hypotheses were formulated and tested:

The SAP measures have no significant effects on the level of cotton production in Nigeria during the study period
The exchange rates of the Naira to the US$ has no significant effects on the level of cotton production in Nigeria during the study period
There is no significant difference in aggregate cotton production in Nigeria before and after SAP in Nigeria
There is no significant difference in average annual capacity utilization of domestic textile industry in Nigeria before and after SAP in Nigeria

MATERIALS AND METHODS

This study utilised time series data on aggregate cotton production, Naira’s average cross exchange rates with the US dollar and average capacity utilization rate of textile manufacturers in Nigeria for the period 1973-2007 to achieve its stated objectives (Appendix Table A1).

Conceptual framework: The conceptual framework for this study is based on the following arguments: First, to achieve SAP’s lofty goal of restructuring and diversifying the productive base of the Nigerian economy so as to reduce dependency on the oil-sector and import, agriculture is expected to play a significant role. Second, two important indicators to monitor the attainment of these objectives are: (i) increase yield in agricultural export crops to reduce dependency on oil as source of foreign revenue, (ii) increase capacity utilization of agro-based industries that utilizes industrial crops as raw material to reduce importation. Third, a significant change in the levels of these indicators will signify the level of impact of SAP.

Estimation of RER: The Real Exchange Rate (RER) was captured using the following proxy (Mamingi, 1997):

(1)

Where:

e=Official nominal exchange rate measured in Naira per US$
WPI= The US Wholesale Price Index
CPI= Domestic consumer price index

The Nigerian cotton WPI was taken as proxy for the US WPI following Harberger (1986), Bautista (1987) and Fosu (1992). The CPI for cotton was assumed constant because cotton is purely an intermediate good.

Model specification and estimation: The impact of exchange rate deregulation on the production of an export/industrial crop such as cotton is assumed its impact on changes in the level of cotton production resulting from changes in exchange rate. The impact of the SAP measures is assumed as its direct effect on the level of cotton production in Nigeria as captured by the dummy variable D.

Consider a typical cotton farm with a production function:

(2)

where, Y is output, X represent variable inputs and Z represent fixed and other shifter variables of the function. Ignoring the fixed costs, the production function becomes:

(3)

Based on the economics of production outlined above, an empirical aggregate model is developed for cotton production in Nigeria, leaving out variables of less interest to this study, as follows:

(4)

where, Yt is cotton production in year t (measured in MT), X1t is exchange rate of the Naira to the US dollar in year t (expressed as a ratio of the Naira to the US dollar), X2t is average capacity utilization of domestic textile industry in year t (expressed in percent) and D is the dummy variable that takes a value of 0 for the years 1973-1985 and 1 for the years 1986-2007.

As noted in various literature, empirical analysis of time series data pose several challenges as empirical work, including causality tests of Granger and Sims based on time series data assumed that the underlying time series is stationary (Seddighi et al., 2000; Enders, 1995; Patterson, 2002). Mercifully, as Gujarati (2003) noted, by simply establishing stationarity of the residuals from regression equation, the traditional regression methodology can be conveniently applied to data involving non stationary time series.

Cointegration was tested on the data collected for this study using the Cointegrating Regression Durbin-Watson (CRDW) Test method as expounded by Gujarati (2003).

Our regression model:

(5)

was estimated and the residuals obtained.

The DW d was computed using the following relation:

(6)

In CRDW Test, the Durbin-Watson d obtained from the cointegrating regression (6) is used, with a proviso that the null hypothesis is d = 0 rather than the standard d = 2 in the conventional DW test for autocorrellation.

The computed DW d (0.856) obtained from the cointegrating regression (5) is greater than the critical value of 0.386 at the 5% level, thus it was concluded that the regression residuals are stationary. However, the estimated DW d value of 0.856 is lower than the critical DW dL value of 1.283, indicating an evidence of positive first order serial correlation (Appendix Table A2).

The first-order difference transformation method was not used to remedy the detected autocorrelation problem because it is not appropriate for our case despite its other advantages. This decision is guided by Maddala (1992) rule of thumb on the appropriateness of using the first-order difference method: use the first difference transformation method whenever d<R2. It will be recalled that our computed d and R2 from Eq. 5 are 0.856 and 0.505, respectively i.e., d>R2.

The Praise-Winsten transformation method, as expounded by Gujarati (2003) was used to transform the model, using ñ estimated based on the Durbin-Watson d statistic. This is done, based on the following assumptions: (a) that the error term in Eq. 5 follows the AR (1) scheme and (b) that if Eq. 5 holds true at time t, it also holds true at time (t-1), thus:

(7)

Multiplying Eq. 7 by p:

(8)

Subtracting Eq. 7 from Eq. 5:

(9)

Where, βt = (μt- ρμt-1)

Equation 10 was then expressed as follows:

(10)

Where, β*1 = β1 (1-ρ), = (Yt-ρYt-1), = (X1t-ρX1t-1), = (X2t-ρX2t-1), β*2= β2 and β*3= β3.

OLS was then applied to the transformed variables to obtain the usual optimum properties of the OLS coefficients asymptotically.

RESULTS AND DISCUSSION

Relationship between Real Exchange Rate (RER) and the average cross exchange of the Naira to the US$:Fig. 1 depicts a graphic representation of how average exchange rate and the values of RER estimated in this study compare over the period 1973-2007.

Fig. 1: Results of regression analysis with transformed variables Eq. 10

Table 1: Results of regression analysis with transformed variables Eq. 10
R2: 0.287, Adjusted R2: 0.218, R: 0.536, F(model): 4.167, p-value for F(model): 0.014, DW d: 0.566, aStatistically significant statistics at both β: 5 and β: 1%

It is clear that the 2 sets of data have almost the same values up to 1985, the pre-SAP period. But beginning from 1986, the SAP period, when the country's exchange rate was first determined officially through a public auction in the foreign exchange market, a slight difference in values between ER and RER could be noticed. From 1989 when the autonomous market and the official foreign exchange market were merged to form a single Inter-bank Foreign Exchange Market (IFEM), the difference in values between ER and RER became pronounced. From 1991 the difference between the two values became glaring, indicating a case of RER volatility. The ER and RER values deviate extensively from one another, indicating distortions in resource allocation. This finding is in agreement with that of Adubi and Okunmadewa (1999), that exchange rate volatility has a direct negative effect on the level of agricultural export trade in Nigeria by causing a decline in export production.

The effects of exchange rates of the Naira to the US$ and SAP on cotton production in Nigeria: The specified aggregate cotton production model is estimated using the transformed time series data for the period 1973-2007 with SPSS 16.0 supported with Microsoft Excel 2007.

The R2 and F values of obtained from our transformed regression Eq. 10 (Table 1), are considearbly smaller than those obtained from our level form regression Eq. 4 (Appendix Table A2). As noted by Gujarati (2003), this is because by taking the first-difference in the course of transforming our regression model, we are essentially studying the behavior of variables around their (linear) trend values. In such case, as observed by Maddala (1992), we cannot compare the R2 and F values of Eq. 7 and 10 because the dependent variables in the 2 models are different. This explains our decision to use the R2 and F values of our level form model (7) in discussing the fitness of our model.

The F value of 10.535 computed for Eq. 7 is highly significant. This implies that the included explanatory variables (annual average exchange rate of Naira to the US$, average annual capacity utilization of domestic textile industry and the effects of the SAP measures on cotton production as represented by the dummy variable) together significantly explain the variation in aggregate cotton production. The R2 value obtained from the equation is 0.505. This indicates that the explanatory variables included in the model explained, on the average, more than 50% of the variation in the total aggregate cotton production over the study period. The unexplained variation, less than 50%, in the model is attributable to other factors not specified in the model due to difficulties in quantification and availability of relevant data.

All estimated parameters of our regression Eq. 10, with the exception of that of the dummy variable D, were statistically not significant at the 5% level. The t value of 2.986 obtained for the coefficient of the dummy variable D from the regression equation is found to be significant when viewed in relation to its computed p-value of 0.005, hence the formulated null hypothesis is rejected. The statistically significant coefficient of the dummy variable indicates that the SAP measures have a positive impact on cotton production in Nigeria. This finding is in agreement with Adubi and Okunmadewa (1999) that increased in production of export crops followed the adoption of SAP in Nigeria.

The statistically not significant estimated exchange rate parameter (-0.574) with a p-value of 0.570 at the 5% level indicates that exchange rate deregulation per se has no significant effect on cotton production in Nigeria. This finding contradicts Abolagba et al. (2010) that exchange rate deregulation leads to increase production of export crops. However, this contradiction could be explained away as resulting from the double-edged sword characteristic of exchange rate deregulation, one of the lessons enumerate from this study as highlighted in the conclusion section of this paper.

The statistically not significant estimated average capacity utilization of domestic textile industry parameter (0.982) with a p-value of 0.334 at the 5% level indicates that domestic utilization has no significant effect on cotton production in Nigeria.

Impact of the structural adjustment programme on the production and utilization of cotton: As mentioned earlier and for the reasons advanced, the transformed regression Eq. 10 yielded parameters with considerably lower values. This informed our decision to utilize the Student’s t test technique for comparison of means of independent samples, at the 5% level of significance to test hypotheses (iii) and (iv). For a description of the Student’s t test technique, by Lehmann (1991), Hogg and Craig (1995) and Keller and Warrack (2003). Sulaiman and Ja’afar-Furo (2010) demonstrated the application of the Student’s t test technique in socio-economic research.

From the results of the Student’s t test (Table 2), the calculated t value of -3.400 is found to be highly significant when viewed in relation to the computed p-value of 0.002, hence the null hypothesis is rejected and it is thus concluded that there is a highly significant difference in mean aggregate cotton production between the pre-SAP period (1973-1985) and post-SAP period (1985-2006).

Table 2: Results of student’s t test

The mean difference of -173.38 indicates that the mean aggregate cotton production in Nigeria in the post-SAP period is higher than the mean aggregate cotton production of the pre-SAP period. The aggregate mean of the pre-SAP period is 167.308 while that of the post-SAP period is 340.691. Thus, more cotton was produced in Nigeria during the post-SAP period. This finding is in agreement with our earlier finding that increased cotton production followed the adoption of SAP in Nigeria.

From the results of the Student’s t-test, the calculated t value of 5.846 is found to be highly significant when viewed in relation to the computed p-value of 0.000, hence the null hypothesis is rejected and it is thus concluded that there is a highly significant difference in average annual capacity utilization of domestic textile industry between the pre-SAP period (1973-1985) and post-SAP period (1985-2006). The mean difference of 26.45% indicates that the average annual capacity utilization of domestic textile industry in Nigeria during the pre-SAP period is higher than that of the post-SAP period. The mean of average annual capacity utilization of domestic textile industry of the pre-SAP period is 71.14% while that of the post-SAP period is 44.69%. Thus, the average capacity utilization of domestic textile industry in Nigeria during the pre-SAP period was higher that during the post-SAP period.

CONCLUSION

This study set out to assess the impact of exchange rate deregulation and SAP on cotton production in Nigeria. The main findings of the study are:

The ER deregulation that follows the adoption of SAP measures resulted in a pronounced deviation between the ER and RER values indicating distortions in resource allocation
SAP measures have a positive impact on cotton production in Nigeria
Exchange rate deregulation per se has no significant effect on cotton production in Nigeria
Domestic utilization by textile manufacturers has no significant effect on cotton production in Nigeria
More cotton was produced in Nigeria during the post-SAP period
The average capacity utilization of domestic textile industry in Nigeria during the pre-SAP period was higher that during the post-SAP period

Based on the findings of this study, the following lessons are noteworthy: Exchange rate deregulation is a double edge sword. On the one hand, successful exchange rate devaluation could lead to increase in producer price which will increase producer incentives. On the other hand, devaluation could lead to increase in prices of agricultural inputs produced outside the economy like fertilizer, pesticides and machinery which as imported goods have their prices raised by exchange rate devaluation, which in-turn increases costs of input and decreases producer incentive to produce more. Thus, in a mineral resource dominated economy like Nigeria, exchange rate has little or no effect on export crop production.

The availability of adequate and functional infra-structure is critical for the success of SAP vis-α-vis the enhancement of the productivity of domestic agro-based industries is such a way as to reduce dependency on imports. The absence of supporting infra-structure, especially electric power supply in Nigeria, increased the operational costs of domestic textile manufacturers thereby making their products less competative both for export and against imported textile materials. As a consequence, most textile manufacturers were sent out of business, and their teeming employees rendered jobless. It has been mentioned earlier, that the textile industry was the second largest employer of labour in Nigeria.

Effective policies to protect domestic agro-based industries in an oil dependent country like Nigeria are a pre-requisite to the adoption of SAP. The absence of such policies in Nigeria, due to its membership of the WTO, makes domestic textile manufacturers vulnerable to the excessive and aggressive competition and probably dumping, from established foreign textile manufacturers.

APPENDIX

Appendix Table A1: Time series data on aggregate cotton production, Naira’s average cross exchange rates with the US dollar and average capacity utilization rate of textile manufacturers in Nigeria for the period 1973-2007
Source: CBN (2009)

Appendix Table A2: Results of regression analysis of level Eq. 4
R2: 0.505, Adjusted R2: 0.457, R:0.711, F (model):10.535, p-value for F(model):0.000, DW d:0.856, aStatistically significant statistics at both β: 5% and β: 1%

1. Introduction

The optimization of the use of water resources is strategic for the long-term competitiveness of the agricultural industry. Indeed, water shortage has become a serious issue, especially in those areas (such as some areas of the Mediterranean Basin) that are exposed to the progressive desertification. On the other hand, in coastal areas, saltwater intrusion (i.e., the movement of saline water into freshwater aquifers [1]), may lead to an increase of the salinity of water, thus making it unusable for irrigation. Water management is considered one of the major challenges of the near future [2]; in fact, by 2030, water demand is expected to be 50% higher than today, and withdrawals could exceed natural renewal by over 60%, resulting in water scarcity [3].

In such a context, it is apparent the importance to develop innovative agricultural systems and to promote technologies that could optimize the exploitation of water resources; nonetheless, it is crucial to guarantee that the appropriate amount of water is timely and efficiently delivered to the plants. As reported in the related literature, a number of solutions to this problem are being considered [4]. For example, prediction models for irrigation are constantly being developed for facing the needs for short- and long-term water resource management [5]. Moreover, Ines et al. [6] combined remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture. Indeed, the severity of the subject has motivated constant interest in individuating effective technological and management solutions for water irrigation optimization [7–10].

Another approach to the optimization of water consumption is the use of superabsorbent polymers (SAPs) [11]. SAPs can absorb and retain extremely large amounts of a liquid (water or an organic liquid) relative to their own mass [12].

In agricultural applications, SAP granules are mixed with the soil in given amounts. After watering, the granules absorb the water by swelling, and then release it slowly through a diffusive mechanism, as the soil gets dry. In this way, irrigated water is not lost through drainage or evaporation while being efficiently supplied to the plant roots when needed. Furthermore, SAP granules increase their size upon swelling, thus enhacing soil porosity and providing a better oxygenation to the roots. A further advantage of SAPs in agriculture is that they can be loaded with nutritional substances and phytopharmaceuticals, which are then gradually released to the plants.

Therefore, employing SAP in cultivations would help not only in minimizing water consumption, but also in rationalizing the use of phytopharmaceuticals, especially in the following agricultural systems:

(A)

Protected cultivations: these cultivations are characterized by great intensity and high specialization of the soil, which ultimately leads to the deterioration of the essential nutrients of the soil, to the accumulation of telluric pathogens and to secondary salinization due to the excessive use of fertilizers and/or brackish water. In this context, not only would the adoption of SAP rationalize the amount of used water, but it would also allow modulating (in time) the supply of nutrients, thus avoiding the accumulation of toxic substances in the soil;

(B)

Soilless cultivations: in soilless cultivations, plants are grown using mineral nutrient solutions in water, without soil [13]. In open soilless systems, there is a massive waste of water and nutrients, which is responsible for an increase in running costs and in contamination of ground and surface water [14]. The adoption of the SAP to ration the delivery of nutrients to the plants would improve the overall environmental sustainability of these systems. Also for closed systems (in which water recirculates), the use of SAP may help hindering water retention of the plants;

(C)

Open-field cultivations: in agriculture, chemically synthesized fertilizers are commonly supplied in larger quantity than actually needed by the plants. Nitrate accumulation in the soil, especially in dry areas, is a typical result of such an excessive use of fertilizers, with negative repercussions on the environment as well as on the product quality, with nitrates being absorbed by the plants and fruit. The presence of nitrates decreases the nutritional quality of the product (especially for leaf vegetables), and increases the risk of developing cancers [15]. The use of certain SAP formulations could allow reducing the amount of fertilizers used and/or limiting nitrate accumulation in the soil.

Pioneering experiments carried out by a Japanese company in the Egyptian desert, in the early 1990s, demonstrated the potential of synthetic SAPs for water management in agricultural applications [16]. Similarly, Woodhouse and Johnson [17] used synthetic SAPs as soil conditioners to aid plant establishment and growth in drought-prone growing media. The most efficient and most widely employed SAPs are based on polyacrylates, i.e., non renewable materials derived from petroleum industry that are reported to degrade at rates less than 10% per year, via delamination, shear-induced chain scission and photosensitive chain scission [18]. Because of their very low degradation rate, acrylic SAPs are regarded as potential pollutants for the soil. Moreover, there are also some concerns regarding the release of toxic molecules during their slow degradation. As a result, in the last decade, the increasing interest in environmental issues has led manufacturers and researchers to focus on the development of alternative, environmentally friendly SAPs. Examples of investigated biopolymers for the synthesis of SAPs include cellulose derivatives [19] and starch [20], which can be degraded by soil microbes [19,21].

In this work, an innovative SAP, obtained through chemical crosslinking of cellulose derivatives, was synthesized, based on the results of previous studies [22–30], and its water retaining capability was assessed with a specific focus on agricultural applications. The goal of this work was to characterize, from an operative point of view, the beneficial effect of the use of the novel SAP in combination with different types of soils, for the cultivation of plants typical of the Mediterranean area. To this purpose, preliminary experiments were carried out to evaluate the absorption capacity of the SAP. Successively, for assessing the efficiency of the SAP after several water absorption/desorption cycles in the soil, the SAP was tested (in different concentrations and at different depths in the soil) for the cultivation of plants inside pots. Finally, the SAP was employed in conditions that mimicked open-field cultivations, in order to analyze its effect on the growth of plants.

2. Materials and Methods

In order to test the suitability of the cellulose-based SAP for use in agricultural applications, the following experiments were carried out:

(i)

Characterization of the absorption capacity of the SAP in both distilled and tap water;

(ii)

Evaluation of the effect of the SAP when placed in different types of soil and at different depths, through experiments performed in plant pots; and

(iii)

Evaluation of the effect of the SAP in conditions that resembled open-field cultivations.

2.1. Synthesis of Cellulose-Based SAP

For the synthesis of the SAP, cellulose derivatives meeting food and pharmaceutical standards were used. In particular, Carboxymethylcellulose sodium salt (CMCNa) and hydroxyethylcellulose (HEC) were the precursors adopted for the hydrogel synthesis. The detailed description of the synthesis process of the used SAP, which is based on the results obtained in previous works [23–30], can be found in [22]. The final powder size distribution of the SAP was in the range 0.1–1 mm. Cytotoxicity tests performed in previous studies showed that the SAP does not exhibit any toxicity [31].

2.2. Preliminary Characterization of the Absorption Capacity of the SAP

For the intended application, it is important to study and characterize the absorption characteristics of the material in water and in saline solutions with different ionic strenghts, thus simulating the actual conditions in which the SAP is used (namely, in contact with nutrients, fertilizers, and soil). A preliminary experiment was conducted to assess the absorption capacity per gram of the used SAP. To this purpose, two samples of SAP weighing approximately 1 g were added with 100 g of water (Wadd) each. After 24 h, during which the SAP had reached water saturation, each sample was passed through a membrane filter. This allowed to separate the excess water (i.e., the water that had not been absorbed), whose weight (Wnot−abs) was measured through a precision electronic balance. The weight of the water absorbed by the SAP (Wabs) was then evaluated as Wadd-Wnot−abs.

A second experiment was performed to evaluate the effect of the environmental electrical conductivity on the swelling properties of the SAP. Indeed, due to the polyelectrolyte nature of CMCNa, the absorption capacity of the SAP strongly depends on the dissolved salts that are present in the solvent: the higher the amount of salts, the lower the absorption capacity. This aspect is crucial for agriculture-related applications. Generally, the electrical conductivity of dry soils is almost negligible. However, the value of electrical conductivity increases with the water content in the soil, due to the progressive dissolution of salts. Furthermore, it is necessary to take into account the electrical conductivity of water itself and of fertilizers that are typically used in agriculture [32]. In this work, comparative absorption tests were carried out by mixing 4 g of SAP with both distilled and tap water. The electrical conductivity of tap water was 1000 μS/cm, as measured through a conductivity meter (model HI 9811-5). Every 24 h for seven days, the excess water was removed from the samples and weighed, as described above. This allowed to assess not only the initial amount of water absorbed by each sample, but also the decrease of water-saturation (sSAP) in time.

2.3. Selection of Soils and Types of Plants

To characterize the behavior of the SAP in the agriculture setting, two different types of soil were selected: red soil (whose color is due to the presence of iron compounds), and white soil (which contains a high amount of clay). The chemico-physical characteristics of the used soils are summarized in Table 1. Moreover, two types of plants were chosen for cultivation: chicory (Cicoria otrantina) and tomatoes (Pomodoro di Morciano di Leuca). Such plants are typical of the Mediterranean areas and grow during the summer season, which makes them particularly suitable for testing the effectiveness of the SAP in critical conditions.

2.4. Experimental Setup for the Evaluation of the Effect of the SAP in the Soil

A total of eighty pots filled with SAP-amended soil were prepared. Figure 1a,b show a schematization and a picture of the experimental setup, respectively. As can be seen from Figure 1a, four sets of samples were prepared: two sets were planted with chicory and two sets with tomatoes. Each set consisted of 20 pots (top schematization of Figure 1b). Each 20-pot set consisted of 10 pots filled with red soil and 10 pots filled with red soil (central schematization of Figure 1a). In turn, each subset of 10 samples was subdivided in five pairs: a pre-established weight percentage of SAP (wSAP) was added to each pair of samples, namely 0%, 0.2%, 0.5%, 1.0% and 1.5% of SAP. For each of these pair of samples, in one pot the SAP was mixed with the surface/near-surface soil (psup); whereas in the other pot the SAP was mixed deeper within the soil (pdeep), as depicted in the bottom schematization of Figure 1a.

The effect of SAP on the cultivations was then evaluated according to the following procedure. Each sample was weighed before (i.e., in the dry state) and after watering with one liter of water. Starting from the latter condition, the percentage water content (θs) of each sample was checked regularly gravimetrically, and monitored over 78 days. During this period, further addition of water was performed only when either of the following conditions occurred:

(i)

the value of θs in the sample without SAP (i.e., the reference sample, wSAP = 0) had decreased to 10%; or

(ii)

for all the samples, the evaporated water equaled the percentage of water evaporated from the reference sample.

This experiment, which was carried out inside a greenhouse, allowed assessing whether the presence of the cellulose-based SAP was actually effective in preventing (or at least hindering) natural evaporation phenomena of the supplied water.

2.5. Experimental Setup for Mimicking Open-Field Cultivations

After the plants had been grown in the seedbed, they were transplanted in large boxes, thus somewhat resembling an open-field cultivation. Also this experiment was carried out inside a greenhouse, from late spring to August; hence, the environmental temperature was particularly high.

The experimental setup consisted of eight wooden boxes (with dimensions 1.80 m × 0.80 m × 0.60 m) filled with soil. Four boxes were cultivated with tomatoes and four boxes with chicory. For each type of plant, two boxes were filled with red soil and two with white soil. In turn, for each type of plant and for each type of soil, one box contained plain soil, whereas the other box contained soil with SAP. In each box, eight plants were planted. Figure 2a shows a schematization of the disposition of the wooden boxes. Figure 2b shows a picture of the experimental setup.

It is important to point out that the SAP was added only on the surface-layer of the soil, in correspondence of each plant (5 g of SAP for each plant).

Each box was equipped with a drip irrigation system. In order to test the effectiveness of the SAP, the plants in the “soil-plus-SAP” boxes were watered with half the amount of water that was supplied to the plants without SAP. In fact, every 12 h, each dripper released approximately 0.33 L of water to the plants in the “soil-plus-SAP” boxes and 0.66 L to the plants in the boxes without SAP. The growth of the plants was visually observed over a period of three months.

3. Experimental Results

3.1. Absorption Capacity of the SAP

The swelling tests in distilled water showed that the cellulose-based SAP can absorb water up to 74 times its own weight (Table 2). This value, which is comparable with those reported for other biopolymer- [19] and acrylic-based [33] SAPs, is clearly appealing for the intended application.

The second swelling experiment performed in this study focused on the effect of environmental electrical conductivity on the absorption capacity of the SAP as well as on the subsequent water loss kinetics from the hydrogel. In particular, a comparison was made between a sample of SAP saturated with tap water and another sample saturated with distilled water. Starting from the water-saturated condition, the desaturation of the SAP was then monitored for seven days by weighing the amount of water that was released daily. While the absorption of the SAP in distilled water was about 74 g water/g SAP, as reported above, the absorption in tap water was about 40 g water/g SAP, which fits in the range of the typical absorption capabilities of SAPs in saline media (30–60 g water/g SAP) [32]. Furthermore, as shown in Figure 3, in the first 4 days the desaturation behavior was similar for the two samples, independently of the type of water used. However, starting from the 5th day of observation, the percentage of water released by the SAP saturated with tap water was lower than that released by the SAP saturated with distilled water. This may be an indicator of the fact that, although the SAP absorbs a lower amount of water when it is in an electrically conductive-solvent (such as tap water), its water retaining capability is higher, likely due to the presence of fixed electrostatic charges on the polymer network which contribute favourably to the overall interaction between the solvent and the SAP. This finding thus further suggests the potential of the SAP for agricultural applications.

3.2. Evaluation of the Efficiency of the SAP after Several Water Absorption/Desorption Cycles

The percentage gravimetric moisture content θs of the soil in the pots was monitored regularly. The addition of water to the pots was necessary only at the following time points:

(1)

on the 36th day, one litre of water was added to both types of soil;

(2)

on the 57th day, 0.8 L of water was added only for the red soil;

(3)

on the 64th day, 0.5 L of water was added only to the clayey soil.

For the two types of soil, and for each concentration of SAP, Figures 4 and 5 show the variation of θs during the observation period, for different values of wSAP. In the figures, the abrupt changes of θs correspond to the aforementioned additions of water.

From Figures 4 and 5, it can be seen that value of θs in the pots free of SAP is always consistently lower than in the pots containing SAP. This confirmed that the presence of the SAP had a beneficial effect in hindering water evaporation phenomena and in providing a higher quantity of water over a longer period of time. Furthermore, at any given day of observation, it can be seen that a higher amount of SAP (hence, a higher value of wSAP) generally led to a higher amount of moisture content in the soil. Indeed, for red soil, this effect was not evident for the case of wSAP = 1.5%; in fact, in this case, the corresponding values of θs were slightly lower than the values corresponding to wSAP = 1.0%. This was probably attributable to a “self-saturation” effect of the SAP, which ended up retaining water within itself (rather than releasing it).

Another important conclusion that can be drawn from these results is that the efficiency of the SAP decreased with time. In fact, for all the wSAP

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