Received date:December 05, 2015; Accepted date: February 09, 2016; Published date: February 17, 2016
Citation: Olaleye AO, Tambi E, Bangali S, Odularu GOA (2016) Likoti Farming under Changing Climate in Lesotho: Agronomic Grain Yield versus Technical Efficiency. J Ecosys Ecograph S5:001. doi:10.4172/2157-7625.S5-001
Copyright: © 2016 Olaleye AO, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Climate Change (CC) and with sub-optimal nutrient contents in the soils of Lesotho is negatively impacting yield and yield components of maize. Often, the grain yield is often ≤ 2.50 t/ha in most cases. One of the ways smallholders in Lesotho try to mitigate the impact of CC and sub-optimal nutrient contents is through the practise of Conservation Agriculture (CA) called likoti farming. Data (soil and socio-economic variables) were collected from smallholder farmers practising Likoti Farming Systems (LFS) in Lesotho. Surface soil (0-20 cm) samples and socioeconomic data were collected between 2012/2013 and 2013/2014 cropping seasons from 105 smallholder farmers in 22 villages. These villages are located in four administrative districts of Lesotho (Berea, Butha Buthe, Leribe and Maseru). The socio-economic variables were on inputs (farm size, payment for land, quantity of fertilizer, fertilizer cost, quantity of seeds, man-days, wage rate, quantity of herbicides, price of herbicides and labour cost) and output (grain yield) used by the farmers. In addition, long-term monthly rainfall data (1900-2007) were collected from the Lesotho Metrological Services. Socio-economic data were analysed using means procedure of SAS across the villages. In addition, these same data (i.e., inputs and output) were subjected to Data Envelopment Analysis (DEA) using SAITECH DEA-Solver under constant returns to scale (CRS). Results showed that soils across these villages/ districts had sub-optimal contents of N, P, K and acidic pH (i.e., ≤ 5.0). The mean annual rainfall has been declining steadily over the years. Examination of the grain yields across villages showed that highest grain yield (e.g. 2113.0 kg/ha) was recorded in Ha-Ts’alemoleka (Butha Buthe District) and the least was about 100 kg/ha in Ha-Khoeli village (Maseru district). When the DEA method was used to examine the same data set using inputs and output, only four (or 18.20%) of the LFS/Decision Making Units (DMUs) were technically efficient (i.e., efficiency score was 1.0), while the other 18 DMUs (or 81.80%) were not efficient. It was observed that agronomic research using grain yields (i.e., output) as the reason why soil conservation is better in one village/region compared to others may be erroneous. Hence, efforts should be made by researchers (i.e., agronomist, soil scientists, animal scientists, extension agents etc..) to use DEA software to evaluate collected data (i.e., input variables) along with the output(s) arising from such trials to make proper decisions to policy makers.
Climate change; Conservation agriculture; Likoti; Lesotho; Maize; Soil
Climate Change (CC) has become a global issue, which has become one of the major themes of discussions by many scientists and policy makers. It is perceived to negatively impact the yield of crops with attendant decrease in the productivity of agriculture in most countries in Sub-Saharan Africa (SSA) and in the whole world. In Lesotho, “likoti” means holes and the farming system was introduced by August Basson, a missionary, who has been living and working in Lesotho for over 30 years. It is a form of Conservation Agriculture (CA) . Since 2005, the Government of Lesotho (GOL) has promoted this farming system as one of the strategies to boost crop production and adapt to the impact of climate change [2,3]. An initial study of likoti farming systems (LFS)  showed that grain yield of maize from Likoti plots were significantly higher than those from conventional farming systems (CFS). Another study sampled 117 likoti farmers in Lesotho along with 112 conventional farmers. The results showed that maize yield from likoti plots were higher than those from the conventional plots . Also, these authors reported that farmers practising LFS were better-off in socio-economic terms than the latter. For this farming system to be scaled-out, Sicili et al.  noted that farmers practising LFS should be properly educated and economic incentives should be provided to such households to help them better adopt the technology. Despite these gains from Lesotho, there has been conflicting results of CA from other parts of the South African Development Community (SADC). For example, these authors Gowing and Palmer ; Nkala et al.  noted that farmers adopting the CA technologies require higher inputs (e.g. herbicides and fertilizer) to make such technologies profitable in Zimbabwe. Nyagumbo, Nkala et al., Musara et al. [6,8,9] reported that adoption of CA improves yield and soil properties in Namibia and Zimbabwe. However, all these studies only considered just one dimension, that is, agronomic yield, but did not consider other household level covariates (e.g. age, sex, farm size, inputs, education, etc.) in evaluating the yield of crops .
“Likoti” practices have been associated with yield gain of maize planted; there is compelling interest to measure and understand the technical efficiency of this technology in the face of declining agricultural productivity among selected smallholder maize farmers in Lesotho. The production of crops, whether under conventional tillage, CA or likoti farming utilizes large quantities of inputs either directly or indirectly in the form of machinery, seeds, fertilizer, manure, chemicals (i.e., herbicides) water, farm size and so on. Excessive utilization of any of these inputs may result in environmental hazards directly or indirectly if used over a long period of time. In addition, some of these inputs are becoming very expensive and often are not available to farmers during the production seasons either due to excessive cost or unavailability. Therefore, efficient utilization of these inputs will reduce environmental hazards, prevent the destruction of natural resources, ensure agricultural sustainability and enable smallholder farmers to utilize agricultural inputs judiciously.
Calculating efficiency and optimizing performance can be achieved using parametric (Stochastic Frontier Analysis- SFA) and nonparametric (Data Envelopment Analysis (DEA) or Free Disposable Hull (FDH) methods. The Data Envelopment Analysis (DEA) was developed by Charnes et al.  as an optimization tool to calculate efficiency. The method has been used to estimate the efficiency in several organizations units in several areas  and across several fields - Agriculture, Health  and Management [14-17]. The advantage is that it places less structure on the shape of the efficient frontier . According to Farrell , the DEA involves the concept of efficiency, which is defined as the ratio of output(s) to input(s). The DEA is a non-parametric method to calculate efficiency, and it involves the use of technical linear programming tools to construct a non-parametric piecewise surface (or frontier) over a given set of data for it to be able to estimate efficiency relative to that specific surface . The DEA develops efficiency by optimizing the weighted output/ input ratio of each Decision Making Units (DMU), subject to the condition that this ratio can be equal, but never exceed, unity for any provider/ in a given data set . Thus, any farm below the frontier is considered to be inefficient (i.e., the scale efficiency is ≤ 1.0). The DEA often constructs a “best-practice” benchmark from data of inputs and outputs supplied . The efficiency score of the best performing technology (i.e., the benchmark) in any given evaluation will only represent the organisation or technology in question considered in the analysis. According to Ozcan , though the DEA can clearly identify improvement strategies for those non-top-performing technologies, further improvements of top performers depend on factors such as new technologies and other changes to such technologies, the price of inputs or the scope of the production process (i.e., scale).
Many studies have reported that LFS improves grain yield of maize, and it also increases the amount of carbon fixed into the soil compared to conventional methods. Despite these gains, there is sparse literature on the technical efficiency of LFS in Lesotho when compared to agronomic yield. The objective of this investigation was to compare the agronomic yield of maize of 105 likoti farmers in 22 villages located in four Districts of Lesotho with results from the efficiency analysis using DEA solver1.
Study sites description
Lesotho is land locked and surrounded only by the Republic of South Africa (Figure 1). It is situated approximately between 280S and 310S latitudes and longitude 270E and 300E. The total land area is about 30, 355 km2 of which about 12% is arable. It is divided into four Agro- Ecological Zones (AEZ): the Lowland, Senqu River valley, Foot-Hills and mountains (Table 1).
|Agro-ecological zones||Altitude (m) above sea level||Topography||Mean annual rainfall (mm)||Mean annual Temperature (ºC)|
|Lowland||<1800||Flat to gentle||600-900||-11 to 38|
|Senqu river valley||1000-2000||Steep sloping||450-600||-5 to 36|
|Foot-hills||1800-2000||Steep rolling||900-1000||-8 to 30|
|Mountains||2000-3,484||Very steep bare rock and gentle rolling valleys||1000-1300||-8 to 30|
Table 1: Agro-ecological zone characteristics of Lesotho.
Between 2012/2013 and 2013/2014 cropping seasons, socioeconomic survey was conducted using structured questionnaires to elicit information from 105 smallholder farmers located in 22 villages practising likoti farming. Data (2010-2015) were collected from these farmers on grain yields (t/ha), amount paid for renting land (Loti2), quantity of fertilizer used (kg/ha), amount spent on purchasing fertilizer (Loti2), quantity of seeds used (kg/ha), man-day spent on the field (hrs), quantity of herbicides used (litres/ha), and cost of herbicides (if any) (Loti). These data were collected using post-graduate students of the National University of Lesotho (NUL) and extension agents working in the Ministry of Agriculture, and those with non-governmental organisations in Lesotho. In addition, surface soil samples (0-15 cm) were collected randomly from 22 of these farmers during the survey (i.e., 2012/2013 - 2013/2014 cropping seasons). These smallholder farmers were located in four administrative districts of Lesotho (i.e., Berea, Butha Buthe, Leribe and Maseru). The soil samples were labelled and bulked together and representative samples were taken for routine laboratory analyses. The soil samples were air-dried for 48 hours and crushed to pass through a 2 mm sieve. The soil samples were analyzed for the following parameters: particle size analysis , Soil pH (water), Organic Carbon (OC) , available Phosphorus (P) , available Nitrogen (N), Bulk Density (BD) was calculated using American Bulk Density Calculator and Soil Organic carbon pool (C-pool) was calculated using relation as given by Wairiu and Lal :
C-pool = d x BD x C-content (1)
Where, C-pool (kg Cm-2), d: soil layer thickness (m), BD: bulk density (kg m-3), C3-content (g g-1). The cation exchange capacity (CEC) was determined using Ammonium acetate at pH 7. The base cations (Ca2+, Na+, Mg2+ and K+) were extracted using 1N NH4OAc and read on Atomic Absorption Spectrometer. Long-term monthly rainfall data (1900 - 2007) were collected from the Lesotho Meteorological station (Figure 2) and annual means of these were calculated for 100 years.
Estimating the efficiency of Likoti farming using DEA
Data Envelopment Analysis (DEA) is a widely used mathematical programming approach for comparing the inputs and outputs of a set of homogenous Decision Making Units (DMUs) [14,15]. The DEA evaluates the performance of DMUs based on evaluating the relative efficiencies of comparable DMUs by estimating an empirical efficient boundary . A DMU (in this case, a farmer/village) is said to be efficient when a comparable DMU can produce more outputs using an equal or lower amount of inputs (e.g. fertilizer, herbicides, man-days etc.). The DEA also provides efficiency scores and reference units for inefficient DMUs. Details of the DEA can be seen elsewhere [13-15,17]. DEA can either be input oriented or output oriented. In the former, the DEA method defines the frontier by seeking the maximum possible proportional reduction in input usage, with output levels maintained at a constant level for each farm. However, in the output orientation, the DEA method seeks to the maximum equiproportional increase in the output production with input levels fixed. Since likoti farming relies on finite and scarce resources; therefore, the input-orientation DEA model was used as it is more appropriate to reduce the amounts of inputs consumed to produce a certain amount of yields of maize [18,27]. As such, the input-orientation method was used to access the efficiency of likoti farming in Lesotho. Thus, the DEA model assume (k=1……, K) decision making units (DMUs), operating in a technology subset T denoted by vector inputs produce a nonnegative vector of output vector The technical efficiency of the k-th DMU, which is a measure by which the k-th DMU is evaluated for its performance relative to the performance of other DMUs in consideration is given by Khshroo et al. :
Where TEk is the technical efficiency score given to the k-th DMU; v and λ denote input and output weights. The efficiency study analyzed using basic Data Envelopment Analysis (DEA)  which is based on the linear programming tools as shown in equation 1 above. The software used was the DEA-Solver (DEA-Solver-PRO version 12.0 – SAITECH, Inc.) [12,28].
Thus, the second stage DEA employed the use of Tobit Regression using efficiency scores derived from the basic DEA model as the independent variable [29,30] and the inputs as dependent variables using STATA . The aim of the second stage DEA is to elucidate factors that affected the efficiency score θ (i.e., set of environmental influences or causal factors) which are beyond the control of farmers. The grain yields of maize across villages were analyzed using the general linear model procedure (PROC GLM) and the Means was separated using the Proc Means .
The physico-chemical properties of the soils collected across the villages are presented in Table 2. Comparing Table 2 with the critical levels for maize (Table 3), the results showed that the soils in the four districts (i.e., across all the villages) are acidic (i.e., ≤ 5.0) . The soil organic matter contents, total N, available P as well as the base cations (i.e., K, Ca, and Mg) were well below the critical levels as reported by Ayodele and Agbool  for maize cultivation (Table 3). As a result, there is need for application of adequate fertilizer which should be based on soil test/critical values (Table 3) in order to prevent environmental pollution and avoid a waste of resources (i.e., fertilizer). An observation of the grain yield presented by villages is presented in Figure 2. The yield of maize among the farmers practising LFS is a function of soil properties, adequate rainfall, absence of pest and diseases, availability of fertilizer in the right amount, and at the right time as well as availability of labour to make the “likoti” holes. Therefore, very low grain yields of maize recorded in Lesotho compared to other countries within the Southern African Development Community (SADC) might be associated with sub-optimal contents of N, P and K coupled with acidic soil pH and declining rainfall (Figure 3). The annual rainfall pattern is bi-modal in Lesotho, but it has been observed that the rainfall season has been shortened, which has negative implications for most shortseason crops (Figure 3) and has been declining over the years (i.e., 1900 - 2007). As such, the cropping calendar has shifted in most of the Agro- Ecological Zones (AEZ) in Lesotho. In order to cope with the shorter cropping seasons and declining rainfall, most farmers have resorted to mitigating these effects by adopting likoti farming. The low grain yields of maize are further exacerbated by high HIV/AIDS scourge in the country as majority of households are headed by women and youths.
|Org. C (%)||0.58 ± 0.3||0.37 ± 0.3||0.48 ± 0.22||0.44 ± 0.3|
|Org matter (%)||1.01 ± 0.53||0.68 ± 0.42||0.71 ± 0.38||0.77 ± 0.50|
|C-pool (kg C m-2)||15.27 ± 10||11.10 ± 7.3||nd||10.75 ± 8.1|
|Exch. Cations (C mol/kg)|
|Ca||1.90 ± 0.86||1.70 ± 1.10||2.44 ± 0.35||4.80 ± 0.30|
|Mg||1.0 ± 0.4||1.71 ± 1.1||1.80 ± 0.56||2.91 ± 1.90|
|K||0.12 ± 0.10||0.46 ± 0.18||0.21 ± 0.02||0.26 ± 0.15|
|Na||0.10 ± 0.01||0.10 ± 0.03||0.04 ± 0.01||0.41 ± 0.40|
|Available P (mg/kg)||0.25 ± 0.15||0.59 ± 0.42||0.50 ± 0.12||0.60 ± 0.45|
|Total N (%)||0.10 ± 0.10||0.15 ± 0.03||0.16 ± 0.10||0.11 ± 0.01|
|Number of Samples||n =19||n =5||n =5||n =16|
Table 2: Physical and chemical properties of the selected districts.
|Soil Properties||Fertility Classes|
|Organic matter (%)||2.00||0- 2.0||2.0-3.0||>5.0|
|Total N (%)||0.15||0 - 0.15||0.15 - 0.20||>0.20|
|Available P (mg/kg)||8.50||0 - 8.50||8.5 - 12.50||>12.50|
|Exchangeable Cations (Cmol/kg)|
|K||0.16||0 -0.16||0.16 - 0.31||>0.31|
|Ca||1.50||0 - 1.50||1.60 - 4.0||>4.0|
Source:Ayodele and Agboola
Table 3: Fertility classes for evaluting maize nutrient levels.
The summary statistics of the variables used for the DEA analysis across the four districts sampled is shown in Table 4. The farm size ranged between 0.80 ha (Leribe District) and 3.69 ha (Berea District) and the cost of acquiring these plots of land also ranged between 3000 (Leribe District) and 18,000 (Maseru District) Loti. Maseru is the capital city and is located within Maseru District. Thus, plots of land are very expensive (Table 4). Other socio-economic variables collected showed high variation across districts (Table 4). It was also observed on the field that most of the soils cropped by farmers in Lesotho are characterised by low amounts of soil organic matter (< 1%), plant available Nitrogen (N), and Phosphorous (P) (Table 3). From this study, it was observed that during the survey, the mean quantity of fertilizer applied, though different, varied across districts and from farmer to farmer ranging between 20 kg/ha (Leribe District) to about 320 kg/ha (Butha Buthe) (Table 4). The only drawback may be the high cost of purchasing these inputs as well as availability at the right time coupled with prevailing government policies on fertilizer usage. In practice, sustainable agriculture uses fewer external inputs (e.g., purchased fertilizer) and more locally available natural resources [34,35]. Low soil N and P availability may be two of the major constraints to crop production in Lesotho as most soils are acidic in pH (i.e., ≤ 5.0). This is exacerbated by soil fertility depletion through nutrient removal as most of the harvested crop residues are not returned back on to the soil, but are either used as forage to feed animals or to build houses. Therefore, majority of farmers may not be able to compensate for these losses that result in negative nutrient balances on their farms . In addition, the use of organic nutrient sources is also constrained by labor availability for collecting and applying these materials as there is competing demand for these crop residues for fuel, fodder and thatch for building houses.
|Variables||Category||Observation (N)||Mean||Std. Dev||Min||Max|
|------------------------------------ Berea District---------------------------------------------------|
|Grain yield (kg/ka)||Output (O)||30||1218.19||1053.24||150||4500|
|Farm size (Ha)||Input (I)||30||3.69||5.34||0||24.03|
|Payment for land (Loti)||Input (I)||30||1550.89||1778.01||0||5000|
|Fertilizer used (kg/ha)||Input (I)||30||137.19||115.27||0||400|
|Fertilizer cost (Loti)/season||Input (I)||30||88.25||83.24||0||290|
|Quantity of seeds used (kg/ha)||Input (I)||30||15.27||14.39||0||50|
|Man-days (hrs)||Input (I)||30||37.42||32.02||0||100|
|Wage rate/day||Input (I)||30||18.27||24.22||0||100|
|Quantity of herbicide used (Litres/ha)||Input (I)||30||0.26||0.44||0||2|
|Price of herbicides (Loti)/season||Input (I)||30||24.91||32.03||0||113.33|
|Labour cost (Loti/season)||Input (I)||30||973.27||1599.71||0||6300|
|------------------------------------ Butha Buthe District -----------------------------------------|
|Grain yield (kg/ka)||Output (O)||45||1850||1559.65||450||4000|
|Farm size (Ha)||Input (I)||45||3.5||1.93||2||6.8|
|Payment for land (Loti)||Input (I)||45||4420||3997.75||500||10000|
|Fertilizer used (kg/ha)||Input (I)||45||320||130.38||200||500|
|Fertilizer cost (Loti)/season||Input (I)||45||112.40||66.02||2||160|
|Quantity of seeds used (kg/ha)||Input (I)||45||26||11.40||10||40|
|Man-days (hrs)||Input (I)||45||85||154.54||0||360|
|Wage rate/day||Input (I)||45||14||19.49||0||40|
|Quantity of herbicide used (Litres/ha)||Input (I)||45||0.35||0.49||0||1|
|Price of herbicides (Loti)/season||Input (I)||45||28||4702.34||0||100|
|Labour cost (Loti/season)||Input (I)||45||2480||0.284||0||10800|
|-------------------------------------------- Leribe District -----------------------------------------|
|Grain yield (kg/ka)||Output (O)||15||610||569.43||150||1600|
|Farm size (Ha)||Input (I)||15||0.80||0.83||0||2|
|Payment for land (Loti)||Input (I)||15||860||1244.19||0||3000|
|Fertilizer used (kg/ha)||Input (I)||15||20||20.91||0||50|
|Fertilizer cost (Loti)/season||Input (I)||15||28.60||37.81||0||70|
|Quantity of seeds used (kg/ha)||Input (I)||15||8||8.37||0||20|
|Man-days (hrs)||Input (I)||15||15||15.65||0||35|
|Wage rate/day||Input (I)||15||17||0||0||30|
|Quantity of herbicide used (Litres/ha)||Input (I)||15||0||0||0||0|
|Price of herbicides (Loti)/season||Input (I)||15||0||463.54||0||0|
|Labour cost (Loti/season)||Input (I)||15||435||0.361||0||1050|
|--------------------------------------------- Maseru District -------------------------------|
|Grain yield (kg/ka)||Output (O)||15||896.88||800.51||100||3000|
|Farm size (Ha)||Input (I)||15||2.5||1.89||0.20||7|
|Payment for land (Loti)||Input (I)||15||3368.75||5034.91||100||18000|
|Fertilizer used (kg/ha)||Input (I)||15||117.19||109.82||0||400|
|Fertilizer cost (Loti)/season||Input (I)||15||113.13||48.95||0||180|
|Quantity of seeds used (kg/ha)||Input (I)||15||18.59||11.79||10||50|
|Man-days (hrs)||Input (I)||15||40.25||25.96||0||96|
|Wage rate/day||Input (I)||15||19.06||14.17||0||50|
|Quantity of herbicide used (Litres/ha)||Input (I)||15||0.38||0.306||0||1.26|
|Price of herbicides (Loti)/season||Input (I)||15||103.28||202.08||0||800|
|Labour cost (Loti/season)/season||Input (I)||15||813.44||819.00||0||2880|
Table 4: Summary statistics of input and output variables used across districts for DEA analysis.
The results of the agronomic yield across the 22 villages where likoti is practised are presented in Figure 2. There were significant differences in the grain yields of maize across these villages. Results showed that grain yield ranged from 150 – 4500 kg/ha with a mean of 1218.19 kg/ ha (Berea District) to between 150-1600 kg/ha with a mean of 610 kg/ ha (Leribe District) (Table 4). Examination of grain yields across these villages showed that a village called Ha-Ts’alemoleka (Butha Buthe District) had the highest grain yield of 2113.0 kg/ha and the least was about 100 kg/ha in Ha-Khoeli village (Maseru District). When the DEA method was used to examine the productivity of the LFS across these villages using not only the output (i.e., grain yield), but other inputs (i.e., amount paid for renting land (Loti), quantity of fertilizer used (kg/ha), amount spent on purchasing fertilizer (Loti), quantity of seeds used (kg/ha), man-day spent on the field (hrs), quantity of herbicides used (litres/ha), and cost of purchasing herbicides (Loti), the result was interesting . It showed that about 18.20% (4 DMUs/villages) were technically efficient (i.e., efficiency score was 1.0). The technically efficient “likoti” farmers are located in Berea (i.e., Ha-Qoqo, Ha- Lifotholeng, and Ha-Pelesa villages) and Leribe (Ha-Levi’s Nek village) districts. These villages actually used the minimum amount of input to produce the outputs (i.e., grain yield). The eighteen other farmers (i.e., 81.80%) can be classified as inefficient (Figure 4) as they were using more inputs to produce the same amount of grain yields (i.e., output). Other results of LFS published by other authors considered only agronomic yields; such studies though very informative [1,4,5], did not examine in detail how much inputs were used by the farmers considred. For example, a closer observation of the results presented in Figure 2 showed that in terms of agronomic yield of maize, the best performing Likoti farmers were those in Ha-Ts’alemoleka village (Butha Buthe District) and the least performing was in a village called was Ha-Khoeli (Maseru District). However, an observation of the technical efficiency scores of these likoti farmers in the afore-mentioned villages were 0.625 and 0.312 respectively .
The results of the second stage DEA showed that the following inputs (i.e., amount paid for land (i.e., land rent), man days, wage rate and cost of labour were significantly related to the efficiency score across all the villages/districts (Table 5). This suggests that in order for these farmers to be technically efficient and for likoti farming practices to be adopted, the Government Of Lesotho (GOL) should formulate a policy where farmers adopting these farming practices would pay less for land being purchased and the wage rate would be increased from minimum of 100 loti/day as in the case of Berea district (Table 4). It would be observed that majority of the technically efficient smallholder farmers are located in this district . Thus, it is recommended that in order for smallholder farmers in Lesotho to properly adopt the LFS, the government should reduce the amount of money paid by farmers to rent/own landed property, increase the wage rate of labourers on such farms to encourage LFS among the rural poor.
|Coefficients||Std. Error||t||P>|t|||-95% Confidence Interval-|
|Farm size (Ha)||-0.004040||0.010183||-0.40||0.694||-0.024712||0.0166332|
|Payment for land (Loti)||-0.000023||0.000011||-2.14||0.039*||-0.000044||0.0000019|
|Fertilizer used (kg/ha)||-0.000169||0.000413||-0.41||0.684||-0.001007||0.0006681|
|Fertilizer cost (Loti)/season||0.0002641||0.000638||0.41||0.681||-0.001031||0.0015594|
|Quantity of seeds used (kg/ha)||0.0056878||0.004435||1.28||0.208||-0.003316||0.0146916|
|Quantity of herbicide used (Litres/ha)||-0.008751||0.108157||-0.08||0.936||-0.228320||-0.0082091|
|Price of herbicides (Loti)/season||-0.005234||0.000301||-1.74||0.093||-0.001135||0.2108189|
|Labour cost (Loti/season)||0.0002016||0.000077||2.63||0.013**||0.0000459||0.0000879|
Number of observations = 105; LR Chi2 (10) = 32.63; Prob> Chi2 = 0.0003; Pesudo R2 = 0.9262 Log Likelihood = -1.300; *= significant at 5%; **=significant at 1%
Table 5: Results of Tobit regression analysis using efficiency scores as independent variable.
Climate change (CC) and soils with sub-optimal nutrient contents is negatively impacting the yield of maize in Lesotho. One of the ways smallholders in Lesotho try to mitigate the impact of CC and suboptimal nutrient contents are through the practise of conservation agriculture (CA) called likoti farming. From this investigation, results have shown that it may be erroneous for researchers evaluating the impact any form of conservation agriculture (CA) (i.e., likoti farming) to base their results only on agronomic yield of crops being planted. It would be worthwhile for researchers to try and factor in other household level covariates (e.g. age, sex, farm size, quantity of fertilizer, and herbicides, and amount of labor used etc.) in evaluating the yield of crops  before making decisions on whether such technologies are technically efficient or not. Results has shown that higher agronomic yield of crops (e.g. maize) in this investigation did not translate into higher efficiency or productivity of such farmer(s).
Part of the funding to conduct this investigation was due to support from The Forum for Agricultural Research in Africa (FARA)/ Norwegian Agency for Development Cooperation (NORAD) in support of “Sustaining the Comprehensive African Agriculture Development Programme (CAADP) momentum”.
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