Field measurements

Cereal and cover crop vegetative biomass was cut on 20% of the length of every fifth row (i.e. 4% of the field area). Biomass was left to dry for at least 2 weeks in the field before weighing. Cereal grain production was harvested on the same row as biomass but for the full length of the row (i.e. 20% of the field area). The length of each row, total number of rows and field width were characterized to extrapolate to an area basis. Mulch quantity on cotton field was estimated using a visual scale at sowing, ridging and harvest. The average of these three dates of observation made the final score we used. The visual scale was previously calibrated by taking pictures and weighing different quantities and types of mulch. For 18 plots of sorghum + B. ruziziensis in the ‘‘Extrême-Nord’’ province we had both data of biomass production year n and mulch on soil year n + 1. On these plots we estimated residue retention from year n to year n + 1. Cotton technical management was assessed through interviews with the farmer two or three times per month. For each plot, the technician recorded the cropping management characteristics (operation, date, procedure, intensity, products used and amount). Cotton yield was measured by harvesting and weighing cotton seeds on every fifth row (i.e. 20% of the field area). The length of each row harvested, total number of rows and field width were characterized to extrapolate to an area basis. Weed pressure was ranked on a visual scale from 0 to 10 (Marnotte, 1984) at sowing, ridging and harvest. In case of heterogeneous weed cover, the plot was visually divided in smaller homogenous parts and the final rank was obtained by a combination of the rank weighted by the respective area. Daily rainfall was recorded in each village with a rain gauge. Soil texture was determined by hand using the VS-FAST method (Mcgarry, 2006) on soil samples taken from 0 to 20 and 20 to 40 cm depths.

Start and end of flowering were estimated visually by technicians, and recorded when flowering had started or ended for half of the plants. Not all husbandry indicators were recorded for every plot, depending on the availability of the technician. Thus the comparison between systems was made on a different number of plots for each indicator tested.

Impact of no tillage and mulching practices on cotton production Cameroon

25 2.2.4. Statistical analysis

The comparative experiments were analyzed as multilocation trials, with a linear model for biomass and yield. For the cereal experiment, the following linear model (1) was used:

Yijk = m + ai + bj + (ab)ij + Eijk (1)

where m is the intercept, ai is the effect of treatment i, bj is the effect of farmer’s field j, (ab)ij is their interaction, i.e. non-additive part of their combined effect, Eijk is the residual plot effect and measurement error in plot k. For this cereal experiment, after discarding the incomplete farmer’s plots, the design was balanced and an analysis of variance was performed for each system compared with the conventional. For the cotton experiment, the design was severely unbalanced and incomplete, as not all the treatments were present in each field. Thus the treatment yield means were adjusted for year and field effects. Since year and field can be considered to be drawn at random in potential populations of years and fields, these control factors and their interactions with the crop management were considered to be random. In addition these random effects on cotton yield were considered to be normally distributed. The parameters of the resulting mixed model were then estimated with the REML method, using the procedure Mixed of SAS/Stat^®. The three adjusted means were then compared with three t-tests, and the P-values adjusted for multiple comparisons using Sidak’s method, which is a modification of Bonferroni’s method (Hsu, 1996).

For the comparison of treatments, the Gaussian assumption adopted for the effects of year and field on yield are not valid for crop husbandry indicators.

Thus the linear model (1) was used, with the P-values for F obtained with a permutation test. When cotton yields were significantly different between techniques, yields under each technique were regressed separately for each of them, using a set of 10 crop husbandry and environment indicators chosen on the basis of: (i) their hypothesized effect on cotton yield; (ii) avoiding correlation

between variables; and (iii) a trade-off between the number of variables and the number of plots analyzed, since not all explanatory variables were recorded in every field. The explanatory variables were: soil texture (fine clay, heavy clay, loam, sandy, sandy loam, silty loam), rain (mm) 2 days before and 10 days after the sowing, number of years of supervision of the field by the project, average weed pressure (average from three ranks at sowing, ridging and harvest), number of herbicide sprays at sowing, number of localized herbicide sprays with a shield after sowing, quantity of P and K fertilizer added (in kg ha^-1 of P2O5);

the amount of K was strictly correlated with P since all farmers of the same province used the same NPK fertilizer, quantity of N fertilizer (in kg ha^-1 of N), average soil cover by residue at sowing (in t ha^-1), date of sowing (in Julian days). The data were screened to discard very incomplete records and variables. The screening was performed by coding the data presence as binary variable in a plot X variable table, and then simultaneous sorting of variables and plots with the PermutMatrix program (Caraux and Pinloche, 2005). As the remaining table still had missing data, multiple imputations were used to fill in the missing values with random numbers drawn conditionally on the existing data (Rubin, 1996). Multiple imputations were done using procedure ‘mi’ and results analyzed with procedure ‘mianalyse’ of SAS/Stat^®. Missing values of qualitative variables (such as soil texture) were not replaced, to comply with the multivariate normal hypothesis underlying the multiple imputation technique. For the comparison of the ‘‘end of flowering dates’’ in the cotton experiment, incomplete farmer’s fields have been discarded. The resulting design was complete and a 2-way analysis of variance was performed with each farmer’s field playing the role of a block. This comparison was performed separately for each province and each conventional technique (T and NT) compared with NTM. All statistical analyses were done using SAS version 9.1.3 (SAS Institute, 2004).

Impact of no tillage and mulching practices on cotton production Cameroon

27 2.3. Results

2.3.1. Vegetative biomass production by cereals and cover crops and