Towards a feminization of agricultural labour in northwest Syria

Smallholdings in the rural areas of northwest Syria are a result of land fragmentation that is due to inheritance. Because of rapid population growth combined with land fragmentation, these smallholdings are increasing and cannot sustain the rural households whose sizes and needs are also increasing rapidly. This situation has led to increasing numbers of mates migrating to urban areas in Syria and to neighbouring countries looking for work opportunities. In addition, recent agricultural intensification trends seem to have led to the emergence of a waged labour force which, in the absence of male workers owing to significant rates of migration, is now predominantly female. Agricultural labour use depends upon household characteristics and resources (type of labour used, gender of labour waged/exchanged/familial). The article attempts to present a comprehensive analysis of household labour use in distinctive farming systems in one region of Syria that has undergone great change in recent decades, and examines the changes in the composition of the agricultural labour force. Secondary information, rapid rural appraisals and formal farm surveys were used to gather information on the households in a study area where different farming systems coexist. The results show that the decrease in landholding size, the resulting male migration, and land intensification have resulted in the expansion of female labour in agricultural production, which has been termed in this research a 'feminization of agricultural labour'. This suggests that agricultural research and extension services will have to work more with women farmers and farm workers, seek their wisdom and involve them in technology and tran, fer This is not easy in conservative societies but requires research and extension institutions to take this reality into consideration in their programmes.

The use of bootstrapped Malmquist indices to reassess productivity change findings: an application to a sample of Polish farms

This article assesses the extent to which sampling variation affects findings about Malmquist productivity change derived using data envelopment analysis (DEA), in the first stage by calculating productivity indices and in the second stage by investigating the farm-specific change in productivity. Confidence intervals for Malmquist indices are constructed using Simar and Wilson's (1999) bootstrapping procedure. The main contribution of this article is to account in the second stage for the information in the second stage provided by the first-stage bootstrap. The DEA SEs of the Malmquist indices given by bootstrapping are employed in an innovative heteroscedastic panel regression, using a maximum likelihood procedure. The application is to a sample of 250 Polish farms over the period 1996 to 2000. The confidence intervals' results suggest that the second half of 1990s for Polish farms was characterized not so much by productivity regress but rather by stagnation. As for the determinants of farm productivity change, we find that the integration of the DEA SEs in the second-stage regression is significant in explaining a proportion of the variance in the error term. Although our heteroscedastic regression results differ with those from the standard OLS, in terms of significance and sign, they are consistent with theory and previous research.

Bt cotton, pesticides, labour and health - A case study of smallholder farmers in the Makhathini Flats, Republic of South Africa

This paper describes some of the results of a detailed farm-level survey of 32 small-scale cotton farmers in the Makhathini Flats region of South Africa. The aim was to assess and measure some of the impacts (especially in terms of savings in pesticide and labour as well as benefits to human health) attributable to the use of insect-tolerant Bt cotton. The study reveals a direct cost benefit for Bt growers of SAR416 ($51) per hectare per season due to a reduction in the number of insecticide applications. Cost savings emerged in the form of lower requirements for pesticide, but also important were reduced requirements for water and labour. The reduction in the number of sprays was particularly beneficial to women who do some spraying and children who collect water and assist in spraying. The increasing adoption rate of Bt cotton appears to have a health benefit measured in terms of reported rates of accidental insecticide poisoning. These appear to be declining as the uptake of Bt cotton increases. However, the understanding of refugia and their management by local farmers are deficient and need improving. Finally, Bt cotton growers emerge as more resilient in absorbing price fluctuations.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Assessing the impact of the 'Decoupling' reform of the common agricultural policy on Irish farmers' off-farm labour market participation decisions

This paper assesses the impact of the 'decoupling' reform of the Common Agricultural Policy on the labour allocation decisions of Irish farmers. The agricultural household decision-making model provides the conceptual and theoretical framework to examine the interaction between government subsidies and farmers' time allocation decisions. The relationship postulated is that 'decoupling' of agricultural support from production would probably result in a decline in the return to farm labour but it would also lead to an increase in household wealth. The effect of these factors on how farmers allocate their time is tested empirically using labour participation and labour supply models. The models developed are sufficiently general for application elsewhere. The main findings for the Irish situation are that the decoupling of direct payments is likely to increase the probability of farmers participating in the off-farm employment market and that the amount of time allocated to off-farm work will increase.