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

Rumination in bipolar disorder: evidence for an unquiet mind

Depression in bipolar disorder has long been thought to be a state characterized by mental inactivity. However, recent research demonstrates that patients with bipolar disorder engage in rumination, a form of self-focused repetitive cognitive activity, in depressed as well as in manic states. While rumination has long been associated with depressed states in major depressive disorder, the finding that patients with bipolar disorder ruminate in manic states is unique to bipolar disorder and challenges explanations put forward for why people ruminate. We review the research on rumination in bipolar disorder and propose that rumination in bipolar disorder, in both manic and depressed states, reflects executive dysfunction. We also review the neurobiology of bipolar disorder and recent neuroimaging studies of rumination, which is consistent with our hypothesis that the tendency to ruminate reflects executive dysfunction in bipolar disorder. Finally, we relate the neurobiology of rumination to the neurobiology of emotion regulation, which is disrupted in bipolar disorder

Evaluation of some physical properties of an oxisol after conversion of native savanna into legume-based or pure grass pastures

This study evaluated the effects of improved tropical grass and grass–legume pastures on the physical condition of an oxisol, previously covered by native savanna. Two long-term pasture experiments with different grass and legume species and with different stocking rates were used. Measurements were made of bulk density, penetrometer resistance, water retention characteristics, total porosity, pore-size distribution and water infiltration rate. The bulk density of the 0–5 cm and 6–11 cm layers was not affected by any of the treatments. Penetrometer resistance was affected by presence of a legume and stocking rate, but differences were small and of little practical importance. There was no difference in water retention curves between the pure grass and grass-legume treatments. The decline in water content with increasing pF occurred more gradually in the low-stocking-rate treatments than in the high-stocking-rate treatments or the native savanna. Consequently, the amount of plant-available water was smaller in the former treatments. The water infiltration rate was higher under grass–legume than under pure grass pastures and decreased with increasing stocking rate under both types of pastures. Differences in soil physical properties are explained by differences in root systems between the two types of pastures, and by differences in biomass, composition and distribution of soil fauna, especially earthworms. The implications of this work for decreasing soil erosion are stressed

Modifying DSSAT crop models for low-input agricultural systems using a soil organic matter-residue module from Century

In low-input systems, where most nutrients become available from soil organic matter (SOM) and residue turnover, the applicability of DSSAT (Decision Support System for Agrotechnology Transfer) crop simulation models is limited because (i) it recognizes only one type of SOM (i.e., humus) and recently added, but not yet humified, residue; (ii) it does not recognize a residue layer on top of the soil; (iii) newly formed humus is given a fixed C/N ratio of 10; (iv) only one litter pool is recognized for N although three are recognized for C; (v) for residues with C/N ratios <25, the three litter pools for C decompose at a rate that is independent of the residue's N concentration; and (vi) SOM and residue flows are independent of soil texture. A SOM residue module from the CENTURY model was incorporated in the DSSAT crop simulation models, and a residue layer was added on top of the soil. Modifications were also made in the senescence module of CROPGRO, a model within DSSAT, so that senesced material is now added daily to the soil. Evaluation of the model, using a data set of 40 yr of bare fallow, showed an excellent fit [product moment correlation coefficient (r) of 0.983] between simulated and measured values for SOM-C. Soil N from decomposing SOM and residues was evaluated with data from a Brazilian experiment with seven leguminous residue types. By incorporating the CENTURY SOM residue module, DSSAT crop simulation models have become more suitable for simulating low-input systems and conducting long-term sustainability analyses