Determination of effective nodulation in early juvenile soybean plants for genetic and biotechnology studies

Symbiotic fixation of atmospheric nitrogen (N2) is a complex physiological process influenced by the interaction of genetic elements in the higher plant species and rhizobia. No standardized, efficient method is available to critically examine the effect of altering the genetic elements in either component by selection and/or genetic engineering. At planting, seeds of a tropical (‘TGX-4E') and non-tropical (‘Soma') soybean cultivar were inoculated individually in sand-filled Conetainers® in a greenhouse with each of two strains within two rhizobial types (Bradyrhizobium japonicum and cowpea). Six weeks after inoculation, each plant was classified into one of two categories; vigorous plant with dark green leaves indicating effective nodulation and N2-fixation (+), and stunted plant with chlorotic yellow leaves indicating ineffective nodulation and no N2-fixation (-). The results indicated that this non-destructive method could be used to identify major genetic differences in the soybean and inoculant. Therefore, this method could be used to rapidly identify genetic segregants resulting from selection in plant breeding programs and/or genetic engineering. Key words: effective nodulation, rhizobia, tropical soybean type, symbiosis. African Journal of Biotechnology Vol.2(11) 2003: 417-42

Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity

Rising atmospheric CO2 concentrations ([CO2]) are expected to enhance photosynthesis and reduce crop water use1. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments1, 2 and global crop models3 to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to evapotranspiration) for wheat, maize, rice and soybean under elevated [CO2] and associated climate change projected for a high-end greenhouse gas emissions scenario. We find CO2 effects increase global CWP by 10[0;47]%–27[7;37]% (median[interquartile range] across the model ensemble) by the 2080s depending on crop types, with particularly large increases in arid regions (by up to 48[25;56]% for rainfed wheat). If realized in the fields, the effects of elevated [CO2] could considerably mitigate global yield losses whilst reducing agricultural consumptive water use (4–17%). We identify regional disparities driven by differences in growing conditions across agro-ecosystems that could have implications for increasing food production without compromising water security. Finally, our results demonstrate the need to expand field experiments and encourage greater consistency in modelling the effects of rising [CO2] across crop and hydrological modelling communities

Uncertainties in Simulating Crop Performance in Degraded Soils and Low Input Production Systems

Many factors interact to determine crop production. Cropping systems have evolved or been developed to achieve high yields, relying on practices that eliminate or minimize yield reducing factors. However, this is not entirely the case in many developing countries where subsistence farming is common. The soils in these countries are mainly coarse-textured, have low water holding capacity, and are low in fertility or fertility declines rapidly with time. Apart from poor soils, there is considerable annual variability in climate, and weeds, insects and diseases may damage the crop considerably. In such conditions, the gap between actual and potential yield is very large. These complexities make it difficult to use cropping system models, due not only to the many inputs needed for factors that may interact to reduce yield, but also to the uncertainty in measuring or estimating those inputs. To determine which input uncertainties (weather, crop or soil) dominate model output, we conducted a global sensitivity analysis using the DSSAT cropping system model in three contrasting production situations, varying in environments and management conditions from irrigated high nutrient inputs (Florida, USA) to rainfed crops with manure application (Damari, Niger) or with no nutrient inputs (Wa, Ghana). Sensitivities to uncertainties in cultivar parameters accounted for about 90% of yield variability under the intensive management system in Florida, whereas soil water and nutrient parameters dominated uncertainties in simulated yields in Niger and Ghana, respectively. Results showed that yield sensitivities to soil parameters dominated those for cultivar parameters in degraded soils and low input cropping systems. These results provide strong evidence that cropping system models can be used for studying crop performance under a wide range of conditions. But our results also show that the use of models under low-input, degraded soil conditions requires accurate determination of soil parameters for reliable yield predictions

Intensifying Maize Production Under Climate Change Scenarios in Central West Burkina Faso

Combination of poor soil fertility and climate change and variability is the biggest obstacle to agricultural productivity in Sub-Saharan Africa. While each of these factors requires different promising adaptive and climate-resilient options, it is important to be able to disaggregate their effects. This can be accomplished with ordinary agronomic trials for soil fertility and climate year-to-year variability, but not for long-term climate change effects. In turn, by using climate historical records and scenario outputs from climate models to run dynamic models for crop growth and yield, it is possible to test the performance of crop management options in the past but also anticipate their performance under future climate change or variability. Nowadays, the overwhelming importance given to the use of crop models is motivated by the need of predicting crop production under future climate change, and outputs from running crop models may serve for devising climate risk adaptation strategies. In this study we predicted yield of one maize variety named Massongo for the time periods 1980–2010 (historical) and 2021–2050 (2030s, near future) across agronomic practices including the fertilizer input rates recommended by the national extension services (28 kg N, 20 kg P, and 13 kg K ha−1). The performance of the crop model DSSAT 4.6 for maize was first evaluated using on-farm experimental data that encompassed two seasons in the Sudano-Sahelian zone in six contrasting sites of Central West Burkina Faso. The efficiency of the crop model was evidenced by reliable simulations of total aboveground biomass and yields after calibration and validation. The root-mean-square error (RMSE) of the entire dataset for grain yield was 643 kg ha−1 and 2010 kg ha−1 for total aboveground biomass. Three regional climate change projections for Central West Burkina Faso indicate a decrease in rainfall during the growing period of maize. All the three scenarios project that the decrease in rainfall is to the tune of 3–9% in the 2030s under RCP4.5 in contrast to climate scenarios produced by the regional climate model GCM ICHEC-EC-Earth which predicted an increase of rainfall of 25% under RCP8.5. Simulations using the CERES-DSSAT model reveal that maize yields without fertilizer show the same trend as with fertilizer in response to climate change projections across RCPs. Under RCP4.5 with output from the climate model ICHEC-EC-Earth, yield can slightly increase compared to the historical baseline on average by less than 5%. In contrast, under RCP8.5, yield is increased by 13–22% with the two other climate models in fertilized and non-fertilized plots, respectively. Nevertheless, the average maize yield will stay below 2000 kg ha−1 under non-fertilized plots in RCP4.5 and with recommended mineral fertilizer rates regardless of the RCP scenarios produced by ICHEC-EC-Earth. Giving the fact that soil fertility improvement alone cannot compensate for the adverse impact of future climate on agricultural production particularly in case of high rainfall predicted by ICHEC-EC-Earth, it is recommended to combine various agricultural techniques and practices to improve uptake of nitrogen and to reduce nitrogen leaching such as the splitting of fertilizer applications, low-release nitrogen fertilizers, agroforestry, and any other soil and water conservation practices

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models