The effect of fall stubble management on over-winter recharge and grain yield

Non-Peer Reviewe

The effect of variable and uniform N-fertilizer application rates on grain yield

Non-Peer Reviewe

Responsive in-season nitrogen management for cereals

Current nitrogen (N) management strategies for worldwide cereal production systems are characterized by low N use efficiency (NUE), environmental contamination, and considerable ongoing debate regarding what can be done to improve N fertilizer management. Development of innovative strategies that improve NUE and minimize off-field losses is crucial to sustaining cereal-based farming. In this paper, we review the major managerial causes for low NUE, including (1) poor synchrony between fertilizer N and crop demand, (2) uniform field applications to spatially variable landscapes that commonly vary in crop N need, and (3) failure to account for temporally variable influences on crop N needs. Poor synchronization is mainly due to large pre-plant applications of fertilizer N, resulting in high levels of inorganic soil N long before rapid crop uptake occurs. Uniform applications within fields discount the fact that N supplies from the soil, crop N uptake, and crop response are spatially variable. Current N management decisions also overlook year-to-year weather variations and sometimes fail to account for soil N mineralized in warm, wet years, ignoring indigenous N supply. The key to optimizing tradeoffs amongst yield, profit, and environmental protection is to achieve synchrony between N supply and crop demand, while accounting for spatial and temporal variability in soil N. While some have advocated a soil-based management zones (MZ) approach as a means to direct variable N applications and improve NUE, this method disregards yearly variation in weather. Thus, it seems unlikely that the soil-based MZ concept alone will be adequate for variable application of crop N inputs. Alternatively, we propose utilizing emerging computer and electronic technologies that focus on the plant to assess N status and direct in-season spatially variable N applications. Several of these technologies are reviewed and discussed. One technology showing promise is ground-based active-light reflectance measurements converted to NDVI or other similar indices. Preliminary research shows this approach addresses the issue of spatial variability and is accomplished at a time within the growing season so that N inputs are synchronized to match crop N uptake. We suggest this approach may be improved by first delineating a field into MZ using soil or other field properties to modify the decision associated with ground-based reflectance sensing. While additional adaptive research is needed to refine these newer technologies and subsequent N management decisions, preliminary results are encouraging.We expect N use efficiency can be greatly enhanced using this plant-based responsive strategy for N management in cereals

Field-Scale Soil Moisture Pattern Mapping using Electromagnetic Induction

11 páginas, 12 figuras, 2 tablas.Soil apparent electrical conductivity (ECa) responds to time-variable soil properties, such as soil water content (θ), and can therefore be used to characterize the spatial and temporal dynamics of θ at the field scale. When clay content is high and uniform and the θ range small, however, it is not clear whether ECa maps can be used for this purpose. A soil management experiment established in a Vertisol in 1982 was surveyed for ECa on 13 occasions to capture changing soil conditions and to determine the sources of this variability. Less variation with time was found in subsoil than in topsoil ECa patterns, especially within the conventional tillage (CT) plots, in areas with shallow soil, and along the drainage network. Using the 13 ECa relative difference data sets as variables, principal component (PC) analysis showed that the first three PCs explained 90% of their total variance. The time-stable or mean ECa pattern was significantly correlated with PC1 and could also be associated with topography, soil depth, and soil structure but could not be related to a single survey. Topography and soil management could be associated with PC2 and PC3, respectively. Time-stable θ patterns, inferred from 26 surveys, revealed topographical and management characteristics and showed significant relationships (P < 0.001) with ECa–derived patterns like soil porosity and infiltration caused by soil management, topography, and rainfall. Electromagnetic induction sensors were useful for mapping soil spatial variability and changing soil conditions due to management effects and external forcing in uniform clay soils.This work was funded by INIA and FEDER through Grants RTA2006-00058- CO3-02 and PRE-2005, by the Junta de Andalucía though the Grants AGR-2349 and AGR-4782, and by the Ministry of Science and Innovation and FEDER through Grant AGL2009-12936-C03-03.Peer reviewe