Morphology, biomass and nutrient status of fine roots of Scots pine (Pinus sylvestris) as influenced by seasonal fluctuations in soil moisture and soil solution chemistry

A field monitoring study was carried out to follow the changes of fine root morphology, biomass and nutrient status in relation to seasonal changes in soil solution chemistry and moisture regime in a mature Scots pine stand on acid soil. Seasonal and yearly fluctuations in soil moisture and soil solution chemistry have been observed. Changes in soil moisture accounted for some of the changes in the soil solution chemistry. The results showed that when natural acidification in the soil occurs with low pH (3.5-4.2) and high aluminium concentration in the soil solution (> 3-10 mg l(-1)), fine root longevity and distribution could be affected. However, fine root growth of Scots pine may not be negatively influenced by adverse soil chemical conditions if soil moisture is not a limiting factor for root growth. In contrast, dry soil conditions increase Scots pine susceptibility to soil acidification and this could significantly reduce fine root growth and increase root mortality. It is therefore important to study seasonal fluctuations of the environmental variables when investigating and modelling cause-effect relationships.

Cross-hemispheric transport of Central African biomass burning pollutants: Implications for downwind ozone production

Pollutant plumes with enhanced concentrations of trace gases and aerosols were observed over the southern coast of West Africa during August 2006 as part of the AMMA wet season field campaign. Plumes were observed both in the mid and upper troposphere. In this study we examined the origin of these pollutant plumes, and their potential to photochemically produce ozone (O3) downwind over the Atlantic Ocean. Their possible contribution to the Atlantic O3 maximum is also discussed. Runs using the BOLAM mesoscale model including biomass burning carbon monoxide (CO) tracers were used to confirm an origin from central African biomass burning fires. The plumes measured in the mid troposphere (MT) had significantly higher pollutant concentrations over West Africa compared to the upper tropospheric (UT) plume. The mesoscale model reproduces these differences and the two different pathways for the plumes at different altitudes: transport to the north-east of the fire region, moist convective uplift and transport to West Africa for the upper tropospheric plume versus north-west transport over the Gulf of Guinea for the mid-tropospheric plume. Lower concentrations in the upper troposphere are mainly due to enhanced mixing during upward transport. Model simulations suggest that MT and UT plumes are 16 and 14 days old respectively when measured over West Africa. The ratio of tracer concentrations at 600 hPa and 250 hPa was estimated for 14–15 August in the region of the observed plumes and compares well with the same ratio derived from observed carbon dioxide (CO2) enhancements in both plumes. It is estimated that, for the period 1–15 August, the ratio of Biomass Burning (BB) tracer concentration transported in the UT to the ones transported in the MT is 0.6 over West Africa and the equatorial South Atlantic. Runs using a photochemical trajectory model, CiTTyCAT, initialized with the observations, were used to estimate in-situ net photochemical O3 production rates in these plumes during transport downwind of West Africa. The mid-troposphere plume spreads over altitude between 1.5 and 6 km over the Atlantic Ocean. Even though the plume was old, it was still very photochemically active (mean net O3 production rates over 10 days of 2.6 ppbv/day and up to 7 ppbv/day during the first days) above 3 km especially during the first few days of transport westward. It is also shown that the impact of high aerosol loads in the MT plume on photolysis rates serves to delay the peak in modelled O3 concentrations. These results suggest that a significant fraction of enhanced O3 in mid-troposphere over the Atlantic comes from BB sources during the summer monsoon period. According to simulated occurrence of such transport, BB may be the main source for O3 enhancement in the equatorial south Atlantic MT, at least in August 2006. The upper tropospheric plume was also still photochemically active, although mean net O3 production rates were slower (1.3 ppbv/day). The results suggest that, whilst the transport of BB pollutants to the UT is variable (as shown by the mesoscale model simulations), pollution from biomass burning can make an important contribution to additional photochemical production of O3 in addition to other important sources such as nitrogen oxides (NOx) from lightning.

Measuring productivity growth when technological change is biased - a new index and an application to UK agriculture

Productivity growth is conventionally measured by indices representing discreet approximations of the Divisia TFP index under the assumption that technological change is Hicks-neutral. When this assumption is violated, these indices are no longer meaningful because they conflate the effects of factor accumulation and technological change. We propose a way of adjusting the conventional TFP index that solves this problem. The method adopts a latent variable approach to the measurement of technical change biases that provides a simple means of correcting product and factor shares in the standard Tornqvist-Theil TFP index. An application to UK agriculture over the period 1953-2000 demonstrates that technical progress is strongly biased. The implications of that bias for productivity measurement are shown to be very large, with the conventional TFP index severely underestimating productivity growth. The result is explained primarily by the fact that technological change has favoured the rapidly accumulating factors against labour, the factor leaving the sector. (C) 2004 Elsevier B.V. All rights reserved.

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.

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.

Seeds of growth? Agricultural productivity and the transitional dynamics of the Ramsey model

A two-sector Ramsey-type model of growth is developed to investigate the relationship between agricultural productivity and economy-wide growth. The framework takes into account the peculiarities of agriculture both in production ( reliance on a fixed natural resource base) and in consumption (life-sustaining role and low income elasticity of food demand). The transitional dynamics of the model establish that when preferences respect Engel's law, the level and growth rate of agricultural productivity influence the speed of capital accumulation. A calibration exercise shows that a small difference in agricultural productivity has drastic implications for the rate and pattern of growth of the economy. Hence, low agricultural productivity can form a bottleneck limiting growth, because high food prices result in a low saving rate.

Productivity change in Polish agriculture: an illustration of a bootstrapping procedure applied to Malmquist indices

This article illustrates the usefulness of applying bootstrap procedures to total factor productivity Malmquist indices, derived with data envelopment analysis (DEA), for a sample of 250 Polish farms during 1996-2000. The confidence intervals constructed as in Simar and Wilson suggest that the common portrayal of productivity decline in Polish agriculture may be misleading. However, a cluster analysis based on bootstrap confidence intervals reveals that important policy conclusions can be drawn regarding productivity enhancement.

Long-term effectiveness of sowing high and low diversity seed mixtures to enhance plant community development on ex-arable fields

Questions: How is succession on ex-arable land affected by sowing high and low diversity mixtures of grassland species as compared to natural succession? How long do effects persist? Location: Experimental plots installed in the Czech Republic, The Netherlands, Spain, Sweden and the United Kingdom. Methods: The experiment was established on ex-arable land, with five blocks, each containing three 10 m x 10 m experiment tal plots: natural colonization, a low- (four species) and high-diversity (15 species) seed mixture. Species composition and biomass was followed for eight years. Results: The sown plants considerably affected the whole successional pathway and the effects persisted during the whole eight year period. Whilst the proportion of sown species (characterized by their cover) increased during the study period, the number of sown species started to decrease from the third season onwards. Sowing caused suppression of natural colonizing species, and the sown plots had more biomass. These effects were on average larger in the high diversity mixtures. However, the low diversity replicate sown with the mixture that produced the largest biomass or largest suppression of natural colonizers fell within the range recorded at the five replicates of the high diversity plots. The natural colonization plots usually had the highest total species richness and lowest productivity at the end of the observation period. Conclusions: The effect of sowing demonstrated dispersal limitation as a factor controlling the rate of early secondary succession. Diversity was important primarily for its 'insurance effect': the high diversity mixtures were always able to compensate for the failure of some species.