Factors affecting farm productivity: discussion of findings Using multivariate analysis, the yields of four common crops were regressed

on a range of biophysical and socio-economic variables; a total of 27 variables (Table 4.1a Chapter 4). The model was a good fit for rice, moderately for abaca, and weak for coconut and sweetpotato. As indicated by the standardized , the relative importance of the predictor variables could be identified and estimated.

Biophysical and technology factors Land quality

Soil type and slope of the farm were the land quality variables found significant for coconut and sweetpotato. The regression used mainly the reported soil texture (e.g. sandy, loam) and slope of farm as proxy for land quality. Reported soil type was found significant only for rice. Growing rice in loamy soils (SOILTYP2) more likely produces better yield. In general, the non-significance for the other crops could mean that yield differences could not be distinctively explained by soil type because farmers' description and reporting for each of the crop did not vary. Results of the soil analysis of the farms of case households clearly showed that soil quality (e.g. pH, chemical

properties) varied across different areas in the villages associated with different land uses (Tables 6.2 and 6.3, Chapter 6).

Slope as a variable is very much related to soil quality because it affects erodibility of the soil as well as water holding capacity. Slope also indicates fertility as erosion carries topsoil and nutrients down to settle on the lower slopes. The main land uses on different slope locations are coconut, at times mixed with some trees, and sweetpotato-corn rotation or sequential cropping.

Location of the coconut farm on the slope was a highly significant predictor.

Those located on the lower slopes with relatively fertile soils and better water holding capacity tended to have better yields. For the same reason, slope was highly significantly related with sweetpotato.

Season

Cropping season was found a highly significant variable for irrigated rice because it is highly affected by the monsoon months. In Plaridel, the May-October growing season (SEASON2) caused the variability in yields as the southwest monsoon affects moisture availability and crop stand (effect of strong winds) of rice. In Alegre, this meant variability of rice yields due to inadequate moisture that limits plant growth. This results in low yields and lesser production for the year because many farmers would not grow rice during this season. They grow instead other food crops such as taro and sweetpotato, which are relatively tolerant to inadequate moisture and do not require much investment.

The reference period (i.e. June, 2001-May 2002) of the survey was a normal year for the cash crops, with no strong typhoons for the past eight years which are the main causes for yield declines. Thus, harvesting during the dry and wet seasons, usually two (i.e. abaca) to four (i.e. coconut) times over the year did not reflect yield variability.

Cropping pattern

This was highly significant and positively related with abaca productivity. It is highly likely that a farmer who has abaca as a monocrop would have better yield than when the area is mixed with other crops, due to the competition with soil nutrients. This was clearly evident in abaca because of the distinct diversity of crop systems in the abaca areas where farmers try to also grow food crops, such as rootcrops and banana. This is a system that farmers practice with one or more plots in order to optimize time and labour in the distant abaca farms. However, the yield differences are not as important to them as they are more concerned that they, too, can grow food crops for subsistence. The same phenomenon was found in coconut farms, but the evidence for the latter is not conclusive, most likely because of less and indistinct diversity in the latter.

172 Resource environments and farm productivity Table 7.1 Summary of productivity regression analysis for cash crops

Variables Abaca Coconut Rice Sweetpotato

R/ R² / Adjusted R² .667 / .445 / .284 .434 / .188 / .082 .859 / .737 / .665 .495 / .245 / .190 df / F / Sig. 58 / 2.773 / .006 121 / 1.773 / .052 51 / 10.212 / .000 44 / 4.446 / .009

Standardized t (Sig.) Standardized t (Sig.) Standardized t (Sig.) Standardized t (Sig.) Constant

(Unstandardized ) (-2300.35) -1.932 (.060) (6009.274) 1.942

(.055) (9913.099) 3.394

(.002) (10862.666) 4.161 (.000) Income from farm

>50%

(LIVETYP 2)

x¹ X -.196 -1.473

(.144)* x x x x

Income from off-farm

>50%

(LIVETYP 3)

-.256 -1.835

(.073)** -.165 -1.528

(.130)* x x x x

Income from non-farm

>50%

(LIVETYP 4)

-.209 -1.595 (.118)* -.323 -2.293

(.024)*** .191 1.758

(.086)** x x

Age of household head

(AGEYRS) x X x X x x -.319 -2.263

(.029)***

Education of household head

(HHEDUC)

-.196 -1.517 (.136)* x X x x x x

Consumer-worker ratio

(CWRATIO) .225 1.722 (.092)** -/073 -.667 x x x x

Years in farming

(NFYRS) .158 1.241 .164 1.527

(.130)** .344 3.509

(.001)*** x x

Loam soil

(SOILTYP2) x x x X .161 1.771

(.084)** x x

Slope of field .159 1.224 -.243 -2.194 x x -.331 -2.355

173 Chapter 7

(SLOPE) (.030)*** (.023)***

Irrigation

(IRRIG) x x -.092 -.898 .235 2.011

(.051)*** x x

Variety of crop

(VARIETY) x x -.084 -.914 x x .240 1.698

(.097)**

Owner-cultivator

(TENSTAT2) .095 .752 -.133 -1.026 .255 2.171

(.036)*** x x

Sharetenancy

(TENSTAT9) .315 2.591

(.013)*** -.152 -1.345

(.182)* .202 1.762

(.086)** x x

Family labour value

(PVALHL) x x x X -1.279 -6.638

(.000)*** x x

Production cost

(PTCOST) x x x X 1.542 7.616

(000)*** x x

Cropping pattern

(CPPNG) .565 3.673

(.001)*** .132 1.317

(.191)* x x

Selling price

(SELPRICE) -.183 -1.557 (.127)* -.237 -2.546

(.015)*** x x

Other farm income

(OTFARMINC) x x -.149 -1.593

(.114)** x x

Non-farm income

(NFNETINC) .418 2.729

(.009)*** x X x x

Remittances

(REMITTANCES) -.315 -2.036

(.048)*** x X x x

Wet season

(SEASON2) x x x X -.345 -3.741

(.001)*** x x

Fertilizer use

(FERTUSE) x x x X -.295 -2.259

(.029)*** x x

1 Note: x = not significant

Variety

The non-significance of variety for most crops except sweetpotato reflects the fact that farmers were using one variety for coconut and a very limited selection for abaca and rice. Hence, no variability in yields, which could be attributed to variety differentials, was found. Coconut and abaca have already been long standing crops for at least ten years, and have not changed varieties since then. For sweetpotato, there were distinct differences in yields from cultivar selection. These were the locally selected best cultivar and other native or introduced cultivars. Farmers could easily exchange (or ask) planting materials, which has been the common mode of obtaining them.

Irrigation

As expected, irrigation has a highly significant positive relation with rice productivity. But it is interesting to note that irrigation is not the most important predictor for rice yield increases, accounting only for a seventh and a sixth standard deviation units compared with production cost and value of family labour. This can be indicative of the need to investigate the quality of irrigation water, especially with reference to the regression results on the fertilizer use variable, and the documented problems related to irrigation.

Fertilizer use

The result on fertilizer use in rice is highly significant, but of an unexpected nature where fertilizer uses can more likely decrease yield. This is revealing and could be alarming. A plausible explanation could be that farmers’

fertilizer application is not appropriate. It is common knowledge that farmers do not really follow the recommended fertilizer use because of capital inadequacy. They usually mix urea and complete fertilizer (NPK, 14-14-14) in 30-70 percent of recommended amount. Besides, no soil analysis was done in both villages to serve as basis for fertilization. This finding on the inverse relationship of fertilizer use on rice yield has confirmed the experience of many Asian countries in the process of agricultural intensification associated with the Green Revolution. The inappropriate mixture of fertilizer could re-verse the intended and expected increased yield, or that the inappropriate fertilizer mixes could result in water contamination, thus reducing the yield (Pinstrup-Andersen, 2003). This certainly has implications for extension activities on crop fertilization and the urgent need to convey to farmers and research-extension workers that the current practice could only be wasteful.

This finding urges immediate investigation and action.

Household characteristics and socioeconomic factors Age of farmer

Age of main farmer had a highly significant negative relation with sweetpotato yield. Sweetpotato is basically a home food crop, with only the surplus sold in Plaridel. But, it is a cash crop in Alegre. Such double function, the crop’s tolerance to adverse climatic condition, the need to provide for a still developing household, and less demanding in terms of production inputs

attract the relatively younger households in growing the crop more intensively than their parents or older households. It is usually the practice of households, in the earlier and growing phases of the family development cycle to apportion one or more of their field plots to sweetpotato, which are commonly intensively farmed by the relatively younger female spouses. All these contribute to the more likely better productivity among younger than older farmers.

Education of household head

With abaca, education of household head was statistically significant but non-conclusive. The negative relationship could be explained via the effect on intensity of labour inputs devoted to abaca, from production to processing activities. The less educated farmers tended to have relatively intensive abaca farming by applying more labour inputs, compared to those who had higher education, and thus could shift labour away from the farm to other economic activities. Number of years in formal schooling (proxy for education) per se is not a relevant variable for productivity. Skills and farming experience would more likely be relevant, though selectively, as the evidence suggests.

Dependency ratio

Defined as the consumer-worker ratio (CWRATIO), the dependency ratio showed a significant and positive relationship with abaca yield. Among all the crops, abaca is the one which can assure farmers of a relatively more dependable cash income, usually in bulk. With more mouths to feed and maintain, farmers would have more motivation to rely on intensified abaca farming for cash income, which tends to improve productivity. As the ratio increases, the intensity of farming is likely to decrease because the distance of farm lands becomes a deterrent. Some households in fact left some farm lands unattended, or went there only once a year.

Years in farming

Number of years in farming was found to be highly significant and positively related with rice, less significant with coconut. Years in farming is a variable that embodies the skills from experience that affect farm management of the crops: more years of experience meaning better cultural management skills that improved yields. This is more critical with rice farming, most probably because of the sensitivity of the crop to timing and skills requirements of the different operations involved, i.e. transplanting, planting, weeding, fertili-zation, and the care that these should be followed because of the importance of the crop as staple food. These are skills-sensitive activities.

Land tenure

The productivity of abaca was found to be highly significant and positively related with land tenure arrangement, which is of share tenancy (TENSTAT9).

About 41 percent of abaca farmers are tenants. These are usually long established tenancy arrangements built up by the older generation, handed

down to the younger and middle-aged farmers. Thus, the incentive to invest family labour to improve the productive capacity of the land is the same as that of own land with security of tenure.

A negative relationship was found with coconut, though not conclusive.

In this case, the lesser intensity of labour use on coconut farms largely explain the lower coconut productivity because coconut farmers usually allocate their labour between own-farm work (in owned or tenanted farms) and off-farm work. In the focused group discussions and community survey it was found that coconut farmers usually have off-farm employment. To explain the difference in signs, it may be noted that sharing is better in abaca farms than coconut farms, on the average because of the distance of the former. Because of the distance of the abaca farms, it is not as easy as with coconut farms to shift labour to work on other farms. Abaca farmers go to the mountains for at least four days and usually in teams of at least three. In some bigger coconut farms, both land and capital are provided by the owner, and labour by the tenant. The greater gain usually goes to the landowner, which again would be a disincentive to the tenant to improve productivity.

The productivity in rice farms is highly significant and positively related to own-farming system (TENSTAT2), and of lesser significance but still positively related to share tenancy (TENSTAT9). This is distinctly unique with rice because it has a dual purpose for food and income for both owners and tenants. Owners will tend to invest more in production inputs to increase the yield. And because rice is the staple food where work provides cash and in-kind (i.e. kgs of rice) remuneration for tenants there is an incentive to improve output. Staple food sustenance is most important, and working hard for any one season is critical as the next season could be affected by a natural disaster (e.g. drought, flood, typhoon). It can be noted that on the average, 75 percent of rice farmers are sharecroppers. In addition, most tenant rice farmers have worked on farms for quite some time and developed an attachment to the land.

Family labour

A relatively higher value of family labour will have a greater likelihood of reducing rice yield. This means that improved wages of household labour in non-farm opportunities could more likely shift labour away from the rice farm and diminish labour intensity in various stages of crop growth, and thus reducing the yield. This was significant for rice, but not for the cash crops coconut and abaca. In rice farming, it would be relatively easy to hire alternate workers, but the skills or motivation may not be as well as those of the replaced family labour. The physically demanding work in coconut and abaca farms, plus their importance as cash crops which involve both production and postproduction activities, would more likely not shift family labour out of it despite a relatively higher value in non-farm wages. In the villages, the differential was not big enough anyway. So, family workers tend to give priority to farm their own cash crops, abaca and coconut, before working in off-farm and non-farm activities.

Costs of production

Rice yields were found to be very sensitive to cost differentials. Cost of production is highly significant and the most important predictor of its productivity. Of all the crops grown in the two villages, rice is the most cost intensive in the sense of cash outlays which include seed, fertilizer, pesticide, and hired labour. The other crops mainly use combinations of both family and hired labour, with minimal capital. The evidence showed statistically significant positive effect on rice yields with increased cost of production.

This would refer to the combined positive impact of investments in better seed, pesticide, and adequate labour.

Non-farm income and remittances

The investment for the wages and food in abaca farming would become bigger, if farming were intensified by increasing the number of harvests, and the area per harvest, because of the distance, the number of workers in a group, and the length of stay in the mountain farms. The major source for such capital comes from non-farm sources. Thus, non-farm income was found to be a highly significant and very important predictor for productivity.

Remittances, however, tended to have the opposite result in productivity. This more likely suggests remittances function as substitute income source for foregone earnings with less intensified farming, thus less productivity, in abaca. This seems reasonable since abaca farming is a more physically draining and time intensive kind of work. This also suggests that remittances are not used for farm investment but more likely for household consumables or durables. Farmers would more likely substitute labour for day’s off-work, or even relaxation and rest with more remittance receipts. Remittances enable abaca farmers to have a more leisure. Remittance is a relatively important predictor for productivity.

Off-farm income

The harvesting and preparation of copra in the villages follow a distinct periodic pattern for the households involved. This makes working in other farms relatively easy as compared to abaca-farming, also because these farms are not so far away. Over time, a network of alternate off-farm workers simply grew out of the need for semi-skilled workers in certain stages of the production-postproduction activities (e.g. harvesting, collecting, de-husking, drying, sacking). The greater such labour demand, the more likely part of own-farm labour is shifted towards other farms for cash wages. This would more likely reduce own-coconut farm yields. The evidence, however, is not conclusive.

Livelihood type

It must be noted that the variable, livelihood type, refers to the typology of households based on the percentage of their income sources (i.e. farm, off-farm, non-farm), and not the type of income itself as predictor. The livelihood

type (LIVETYP) variable would be reflective of livelihood strategies, and socio-economic status of households.

Farming households relying more on non-income sources (LIVETYP4) for a living would most likely have coconut farms which are less productive.

They are also most likely the farm owners (previously own-cultivated) who would now lease out or sharecrop their farms, and who would use less labour intensity and do less farm monitoring. A distinct pattern emerges: moving away from a greater reliance on farm incomes tends to dampen the productivity of own farms. As the most important predictor, greater opportunities and improved non-farm incomes among coconut farming households could be critical to the productivity of coconut farming in general.

The variable LIVETYP4 was significantly and positively related to rice yields. Non-farm incomes are definitely sources of capital for rice production which require intensive inputs such as seed, fertilizer, pesticide, and labour.

This was a less important predictor than the costs of the inputs themselves.

The variable, however, pointed to the likelihood of decreased productivity of abaca. The evidence though is not conclusive, which is probably be due to the capital-generating function of non-farm incomes that some abaca farmers use in intensifying productive activities. The evidence shows that non-farm income is, in fact, a highly significant and more important predictor for increased abaca productivity.

For households with greater reliance on off-farm incomes or farm wages (LIVETYP3), or starting to move away from farm income reliance (LIVETYP2), results showed some likelihood of decreased productivity of coconut farms. This could more likely be due to the lessened labour intensity on own coconut farms as labour time is diffused, and work apportioned to other farms. The evidence, however, is non-conclusive. For the same type of households, the results showed statistical significance of the greater likelihood of decreased productivity in abaca farms. Labour shifting away from the abaca farms is the most plausible explanation; especially since doing so would take out much from abaca time because of the distance of the farms and the inflexibility of labour time once engaged in those farms. The variable is a relatively important predictor.

Farmgate selling price

Price was found to be highly significant and negatively related with rice yield.

This cannot be considered a price effect, and should be treated with caution because of the limited range of the survey reference period (June, 2001-May, 2002). Seasonal productivity differentials, due to differences in moisture and other climatic effects, will also need to be considered in production.

The relatively small decline in rice price was due to the relatively bigger harvest during the wet season, as was the case in other rice-producing areas in the region. These also produced relatively better than the dry season-yield because of favourable climatic conditions. This is reflected in the season variable, which is a relatively better predictor than selling price. Farmers usually intensify production during the wet season. The opposite is true of

price and productivity during the dry season, with expected lesser yield per area and the price relatively higher in less supply months.

price and productivity during the dry season, with expected lesser yield per area and the price relatively higher in less supply months.

In document Food insecurity in fragile lands : Philippine cases through the livelihoods lens (Page 190-199)