A soil temperature decision support tool for agronomic research and management under climate variability: Adapting to earlier and more variable planting conditions

2019 ◽  
Vol 162 ◽  
pp. 783-792 ◽  
Author(s):  
Jarrod Kath ◽  
Keith G. Pembleton
Keyword(s):  
Decision Support ◽  
Climate Variability ◽  
Soil Temperature ◽  
Decision Support Tool ◽  
Support Tool

Predicting pasture and sheep production in the Victorian Mallee with the decision support tool, GrassGro

2006 ◽  
Vol 46 (8) ◽  
pp. 1005 ◽  
Author(s):  
S. M. Robertson
Keyword(s):  
Decision Support ◽  
Climate Variability ◽  
Field Experiments ◽  
Sandy Loam ◽  
Decision Support Tool ◽  
Soil Types ◽  
Pasture Production ◽  
Support Tool ◽  
Loam Soil ◽  
Sheep Production

The GrassGro decision support tool was designed to quantify sheep and pasture production in response to management and climate variability in temperate Australia, and has been tested in temperate but not low-rainfall Australian conditions. Data from field experiments and from on-farm monitoring was used to test GrassGro predictions of annual and perennial pasture production, and sheep production at 4 locations throughout the Victorian Mallee, which is a low-rainfall area (275–375 mm annually). Predictions of long-term pasture production were then made. Predictions of the herbage biomass of annual pastures closely matched observed data for both a sandy loam (1991–2002 data) and a whole paddock (combining sandy loam and loam and sand) (2001–2002 data) soil type, at several locations across the Victorian Mallee. Linear regression between observed and simulated (April to September) data produced coefficients, significance and root mean square error of r2 = 0.81, P<0.001, 217 kg DM/ha, respectively, for sandy loam soil types and r2 = 0.94, P<0.001, 72 kg DM/ha, respectively, for whole paddock soil types. A series of simulations for individual years from 1970 to 2002 quantified the large impact of climate variability and demonstrated that seedbank and location, but not soil fertility, had a large influence on annual pasture production. However, GrassGro underestimated the production of the perennial pasture, lucerne (r2 = 0.2). GrassGro was also unable to adequately predict sheep production because it failed to take into account the sparse, clumpy structure of the low biomass pastures typical of this region. Methods to improve GrassGro were identified and included: (i) the need to adjust sheep intake from low biomass, sparse pastures, (ii) the ability to predict summer growing and autumn growing plant species, (iii) the ability to graze crop stubbles and (iv) refinements to the coefficients of equations used to model lucerne growth.

scholarly journals Exploring the Potential Impacts of Climate Variability on Spring Wheat Yield with the APSIM Decision Support Tool

2015 ◽  
Vol 06 (07) ◽  
pp. 686-698 ◽  
Author(s):  
Louis Kouadio ◽  
Nathaniel Newlands ◽  
Andries Potgieter ◽  
Greg McLean ◽  
Harvey Hill
Keyword(s):  
Decision Support ◽  
Climate Variability ◽  
Spring Wheat ◽  
Wheat Yield ◽  
Decision Support Tool ◽  
Support Tool

A Decision Support Tool for Evaluating Big Data Investment in Transport: Empirical evidence from European use cases of NOESIS project

2020 ◽  
Author(s):  
Christos Katrakazas ◽  
Natalia Sobrino ◽  
Ilias Trochidis ◽  
Jose Manuel Vassallo ◽  
Stratos Arampatzis ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Empirical Evidence ◽  
Decision Support Tool ◽  
Use Cases ◽  
Support Tool
Sign in / Sign up

Export Citation Format

Share Document