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The economy of Lesotho is mostly dependent on agriculture with a small industrial sector. The type of agriculture practiced is mainly subsistence with minimal commercial farming Agricultural Production Survey (APS) is an annual survey, which is undertaken by the Bureau of Statistics (BOS). APS runs from the 1 st of August to 31 st July of the following year. APS concentrates on the production of both livestock and crops in the rural parts of the country. In addition to APS, Bureau of Statistics (BOS) conducts Crop Forecasting Survey (CFS) as early as April every year. Crop forecasting is a process of estimating the most likely yield and production of a crop. Forecasts assume that there is no change in production of crops between date of forecasting and final harvest. ABSTRACT OBJECTIVES A stratified multi-stage sampling scheme is adopted for the selection of the sample for the Agricultural Production Survey. Two or more enumeration areas are combined to form Primary Sampling Units (PSUs) and individual agricultural holdings (farming households) constitute secondary sampling units (SSUs) for estimation of land use, crop areas and livestock population. Fields planted maize and sorghum form the third sampling units for estimation of crop yield, two subplots for crop cutting in each field form the ultimate units for yield selection. 120 PSUs in rural areas that cover about 2,309 holdings are selected. A maximum of five fields for both maize and sorghum per PSU constitute the sample for the crop forecasting exercise which covers summer season only. The PSUs are selected with probability proportional to size, the size estimate being obtained from the 2006 Population Census. In each PSU, about 25 agricultural households are selected through systematic sampling from a list of all agricultural households Data for Crop forecasting Survey is collected from a sub-sample of 5 fields out of 10 fields per PSU per crop (Maize and Sorghum) that are selected for crop cutting. Data is collected from two sampled sub-plots of 10 square meters each from selected fields. Sub-plots are located using random numbers. A tape measure is used by enumerators to locate the point (P1) along the length of the field, and then enters the field to locate the second point (P2) along the width of the field. Then the first sub-plot is constructed. The same procedure is repeated to construct the second sub-plot. After construction of the sub-plots we then perform the following steps : In plot 1: We count the number of effective plants in that sample-plot, and then count the number of effective cobs. We select five effective plants in the following manner:  Plant 1: We select effective plant nearest to the Center in the sample-plot.  Plant 2: We select effective plant farthest North in the sample- plot  Plant 3: We select effective plant farthest East in the sample- plot.  Plant 4: We select effective plant farthest South in the sample- plot.  Plant 5: We select effective plant farthest West in the sample- plot. After selecting the five effective plants, we measure the length, circumference and fullness of the selected cobs.Out of the five plants of sub-plot one, we remove the cob(s) on the plant in the center and the plants in the north for collecting cob samples. However, in sub- plot two we measure all the cobs on the selected plants and do not take any samples. SURVEY DESIGN Linear Regression Analysis is used to estimate yield per district because it has been established that the size of the cob is related to its weight and it is run for the whole district with the calculated dry weight and the calculated cob size. The regression gives the coefficient of the cob size in the district and the constant. To estimate the yield per district using the regression equation, we calculate total cob area per district and substitute the results in the regression equation which appear as: Y = aX +/- C Where Y= Estimates of yield a= coefficient X= total cob area C= constant The regression relationship is therefore, run between the dry weight which is the dependent variable and cob size which is an independent variable. From the cobs that are collected from the two effective plants we calculate moisture content and the grain weight to obtain the dry weight using the following formula: Dry weight = W * ((100 – mc) / (100 – 13)) Where: W=wet weight of shelled grains. Mc=(%) of moisture content observed from the shelled grains. 13=(%) of moisture content in completely dry grains. 100=the moisture content in completely wet grains. To calculate the cob size the following formula is used: Cob size = (L* C* F)/pi Where L= length of the cob. C= circumference of the cob. F= fullness of the cob. Pi is taken as 3.1416. The dry weight and cob size are calculated individually for all cobs that were collected in the district. We calculate the average number of cobs in the field, PSU and for the whole district. To calculate total cob area per district, we employ the following formula: Tca = Aca * Anc Where: Tca = Total cob area Aca = Average cob area Anc = Average number of cobs The calculated total cob area per district is then substituted in the regression equation (Y = aX+/- C) to give estimates of yield per hectare per district. After we get yield (Y) from Y = aX +/ - C, we convert it to kilograms (kg) by dividing it by 1000, then it is further converted to metric tonnes by again dividing it by 1000. Production is then estimated by multiplying weighted area planted to maize with estimated yield. P = A * Y Where: P = the production A = area planted Y = the estimated yield per hectare ANALYSIS METHODS ANALYSIS METHODS cont. The selection of fields planted to sorghum is the same as that of maize. For sample plot 1: We count the number of effective plants and effective heads. The plants are selected in a similar way as those of maize: one effective plant in the North, one in the Center, one in the East, one in the West and one in the South. We then remove all effective heads from the five selected plants. We repeat the same steps for the second sub-plot. No plant/head is removed from sample plot two. In sorghum crop there is no regression analysis like in maize. To estimate yield per district, we first estimate yield per field in grams. Yield calculated is per 10 meters squares. ANALYSIS METHODS cont. Formula used to calculate yield of each field is as follows: Y = (E P1 + E P2 ) / 2 * (DW / E ESP ) Where Y = estimated yield in grams E P1 = number of effective heads in sub-plot 1 E P2 = the number of effective heads in sub-plot 2 DW = dry weight E ESP = the number of effective heads in five selected plants from sub-plot 1 Dry weight is calculated by shelling all heads from each field, weighing the grains together to get wet weight and dry weight is calculated using the same formula as in the maize crop. After getting the yield (Y) per field then we calculate the average yield per PSU or district. The results are yield per PSU or district in grams. Yield (Y) is then multiplied by weight; which is found using the same procedure as that used in maize crop. Then the result will be yield per hectare. Production is estimated by multiplying area planted with estimated yield per hectare. The formula used is the same as in the one used to calculate maize production: P = A * Y The main purpose of the CFS is to inform planners and policy makers about the expected crop production in order to make effective decisions concerning availability of food in the country and to make necessary preparations if there is shortage of food. Government requires information in advance regarding production as it is an important factor in measuring national income. In countries which are not self sufficient in food like Lesotho, forecasts of local farmers’ production are needed to ascertain the quantities of cereals needed in the country for the following agricultural year. CFS results can also be used by public and the private sectors dealing with agriculture for providing the necessary storage adjustments and for making available credit on the basis of crop prospects or forecasts. Production forecasts are essential to inform all users in the forecasting of prices of agricultural inputs and household food security. BY LIMAKATSO MATSOSO, MACHITJA RAPHOTO AND NOMZWAKHE SEPHOKO BUREAU OF STATISTICS LESOTHO CROP AND YIELD FORECASTING PROCEDURE IN LESOTHO