Reducing the emissions of greenhouse gases (GHG) from paddy fields is crucial both for the sustainability of rice production and mitigation of global climatic warming. The effects of applying industrial and agricultural wastes as fertilizer on the reduction of GHG emissions in cropland areas, however, remain poorly known. We studied the effects of the application of 8 Mg ha−1 of diverse wastes on GHG emission and rice yield in a subtropical paddy in southeastern China. Plots fertilized with steel slag, biochar, shell slag, gypsum slag and silicate and calcium fertilizer had lower total global-warming potentials (GWP, including CO2, CH4 and N2O emissions) per unit area than control plots without waste application despite non-significant differences among these treatments. Structural equation models showed that the effects of these fertilization treatments on gas emissions were partially due to their effects on soil variables, such as soil water content or soil salinity. Steel slag, biochar and shell slag increased rice yield by 7.1%, 15.5% and 6.5%, respectively. The biochar amendment had a 40% lower GWP by Mg−1 yield production, relative to the control. These results thus encourage further studies of the suitability of the use waste materials as fertilizers in other different types of paddy field as a way to mitigate GHG emissions and increase crop yield.

Potassium chloride (KCl) is the most widely used fertilizer in oil palm (Elaeis guineensis) plantations and the rates applied are based on interpretation of leaf K contents. When no positive response on leaf K contents can be detected, no optimum content can be established whatever the yield response to KCl rates. We used data from 13 fertilization trials conducted on several continents to study the responses of leaf K, leaf Cl, leaf Ca and yield to KCl rates as a function of the soil properties of each site. We found that the abundance of exchangeable Ca in the soil expressed as a percent of the cation exchange capacity (CEC) was the best soil variable to predict if leaf K content would increase with KCl rates. In addition, we found that the leaf K contents of unfertilized controls at the end of the trials were also correlated with Ca/CEC. This ratio thus appears to be a better index of soil K reserves than soil exchangeable K content.

Larvae of bollworms (Helicoverpa armigera (Hubner), Earias sp., Diparopsis watersii (Rothschild) and Pectinophora gossypiella (Saunders)) feed on cotton flower buds (squares) and developing bolls causing severe yield losses. While endosulfan, an organochlorine insecticide was the most effective and widely used insecticide for bollworm control in Ghana, it has been banned due to abuse and hazard to the environment. Field experiments were conducted during the rainy seasons of 2012 and 2013 to determine the efficacy of foliar insecticides tihan (spirotetramat + flubendiamide), thunder (imidacloprid + betacyfluthrin), belt expert (flubendiamide +thiaclopride), dursban 4EC (chlorpyrifos-ethyl), lambda super 2.5EC (lambda cyhalothrin) and polytrin C (profenophos + cypermethrin) for control of bollworms and their impact on non-target beneficial organisms in Ghana. All the insecticides tested lowered bollworm densities and boll damage but applications of tihan or belt expert alternated with thunder resulted in the highest seed cotton yield. The treatments generally did not lower populations of predators such as ladybird beetles and lacewings and could be included in an integrated pest management programme for bollworms in cotton. These results suggest that alternate applications of tihan or belt expert with thunder can be recommended as a replacement for endosulfan for control of cotton bollworms and improvement of cotton yield in Ghana.

A formula is developed for calculating the expected gain when a first-order autoregressive repeated measures model for the plot errors is assumed. Using examples from our earlier papers, the similarities of the conclusions about the best selection programme from using simulation of an unstructured model and from using the autoregressive formula for expected gain are presented. The autoregressive formula is then used to derive optimal programmes when the number of plots or plot years is fixed for a range of values for the variance of the interactions of clone effects with years relative to the variance of the clone effects and for the variances and covariances between years of the plot residuals. In general, there are advantages in studying many clones at low replication rather than fewer clones at high replication.

We regret that the computer program used to generate the simulations of the examples presented in Owusu-Ansah, Curnow and Adu-Ampomah, 2013 contained an error. The corrected simulations for trial M2 now show that a single replication of many clones gives slightly higher selection gains than greater replication of fewer clones. The very small advantages of large numbers of years of harvest remain correct. The selection gains from the trials were considerably underestimated and, with the corrected simulations, the expected performance in terms of yield of the selected clones with the right combination of numbers of clones and replicates often exceeded the performance of the best standard clone. The selected clones still underperformed compared with the best standard clone when selection was based on percentage of diseased pods or numbers of healthy pods.

Legumes are essential to meet nutrition need of growing population in Afghanistan but have low productivity under the farmer practices. With an aim to introduce improved varieties of chickpea (Cicer arietinum) and mung bean (Vigna radiata) along with associated cultivation practices, a number of on-farm demonstration trials were conducted in eight districts covering three provinces to assess their performance in comparison with local practices. The data or information generation from on-farm trials is routine but remains unutilized under the commonly used analysis based on frequentist approach. This study makes use of such prior information to widen the statistical inference framework using a Bayesian approach (BA) for analysing any future on-farm trials. The statistical analysis evaluated the mean performance of the improved and local production packages and risks associated with them to meet a given target. Evaluated at the two districts during 2012, the improved variety of chickpea (Madad) yielded higher than the farmer variety (2.25–2.40 vs. 1.93–1.94 Mg/ha), while that of mung bean (Mash 2008) yielded higher than the farmer variety (1.22–1.24 vs. 0.93–0.94 Mg/ha), both with high probability. The stochastic dominance analysis showed that the recommended package with improved variety Madad has 95% probability of achieving a target of 2 Mg/ha while such a target for local practice was around its one-third when cultivated in Baghlan-e-sannati district. Crossover in the risk curves helped to identify and quantify the region specific risk to meet a given target. BA is then recommended in the analysis of on-farm trials data.