Water use and grain yield response of rainfed soybean to tillage-mulch practices in southeastern Nigeria

Despite the agronomic, economic and food values of soybean (Glycine max L. Merrill), there is still dearth of information on the tillage need and the implications of surface mulch for the crop in the eastern part of the forest-savanna transition zone of Nigeria. This study was therefore carried out on a sandy loam Ultisol at Nsukka with a sub-humid climate, during 2006 and 2007 cropping seasons. Our objective was to devise an appropriate tillage method for the crop from evaluated effects of no-till (NT), conventional tillage (CT) and mulch on selected key agronomic indices. Each of the NT and the CT was either unmulched (U) or mulched (M) in a split-plot, giving four treatments/tillage methods (NTU, NTM, CTU and CTM) randomized in four blocks. Rainfall was more favorable in the first than in the second season. The mean seasonal soil water storage (range, 99-109 mm) within 0.5-m soil layer differed among the treatments (NTU < CTU < NTM = CTM). However, for the first and second seasons, both water use (582-616 and 667-709 mm respectively) and grain yield (0.71-0.81 and 1.22-1.91 Mg ha-1 respectively) were not different. Mulch lowered the crop water use but had no influence on grain yield. Water use efficiency was enhanced with mulch only in the second season. Although either of the two mulch treatments (NTM/CTM) would be suitable for growing soybean especially in years of unfavorably distributed rainfall, NTM is a more rational choice than CTM. Rainfall adequacy at the critical reproductive stage of the crop showed to be a more important yield factor than the tested tillage methods

Reconstructed monthly river flows for Irish catchments 1766–2016

A 250-year (1766–2016) archive of reconstructed river flows is presented for 51 catchments across Ireland. By leveraging meteorological data rescue efforts with gridded precipitation and temperature reconstructions, we develop monthly river flow reconstructions using the GR2M hydrological model and an Artificial Neural Network. Uncertainties in reconstructed flows associated with hydrological model structure and parameters are quantified. Reconstructions are evaluated by comparison with those derived from quality assured long-term precipitation series for the period 1850–2000. Assessment of the reconstruction performance across all 51 catchments using metrics of MAE (9.3 mm/month; 13.3%), RMSE (12.6 mm/month; 18.0%) and mean bias (−1.16 mm/month; −1.7%), indicates good skill. Notable years with highest/lowest annual mean flows across all catchments were 1877/1855. Winter 2015/16 had the highest seasonal mean flows and summer 1826 the lowest, whereas autumn 1933 had notable low flows across most catchments. The reconstructed database will enable assessment of catchment specific responses to varying climatic conditions and extremes on annual, seasonal and monthly timescales

Winds of Change: A Century of Agroclimate Research

Climate has been of primary concern from the beginning of agricultural research. Early in the 20th century, climatology and agronomy evolved separately, focusing primarily on production agriculture and crop adaptation. Concepts developed include thermal units and water use efficiency. The integrated discipline of agroclimatology developed in the mid-20th century. As theoretical understanding evolved, numerous papers related to agroclimatology were named Citation Classics. Spectral properties of plants and soils were identified that underpin today’s remote sensing technologies. Commercialization of instrumentation enhanced our ability to efficiently collect data using standardized methods. Private and public-sector partnerships advanced research capacity. Later in the 20th century, research focus shifted toward integrating knowledge into crop growth and agronomic models. Remote sensing provided capacity to gain theoretical and practical understanding of regional scale processes. In the early 21st century, recognition of earth as a system along with inter-related human systems is driving research and political agendas. There is a pressing need to change our data-rich to an information-rich environment. The emerging cyberinformatics field along with natural resource and agricultural system models allow us to apply climate information to assessments and decision support related to water supply, production, environmental management, and other issues. Solutions to today’s problems require interdisciplinary and multi-sectoral teams. While needs have never been greater, fewer universities maintain critical mass required to off er advance degrees in agroclimatology. It will be increasingly important that agrclimatology attract top students and provide training and practical experience in conducting integrated systems research, communications, and team skills