Re-engineering soils to improve the access of crop root systems to water and nutrients stored in the subsoil

Document Type

Collection

Publication Title

Re-engineering soils to improve the access of crop root systems to water and nutrients stored in the subsoil

Abstract

Data includes: Plant estabishment and germination counts Grain yield and harvest index (and tield compnents - physiological maturioty, total weight, grain weight, head number) Soil compaction/depths Soil water content, water infiltration, water repellance, water retention Grain quality Soil sampling (physical and chemical analyses) Pogo analysis Early biomass (dry wt, wet wt) Crop canopy cover (Drone NVDI measurements)/temperature Leaf chlorophyll content Ploughing depth Anthesis biomass and tiller counts Assessment of amelioratiuon efficacy. Paddock yield data, historical satellite imagery NVDI, assessment of yield potential Trial site-year information including: Weather conditions (rainfall and temperature during growing season Nitrogen test resultys Stage of growth of the wheat crop to which the nitrogen was applied Type of vegeation present Soil type, soil sampling analysis of chemical and physical data, historical yield data pre and poast soil amelioration treatments, NVDI, soil penetration resistance

Publication Date

2023

Keywords

acidity, tillage, yields, soil compaction, soil pH, clay soils, ploughing depth, soil chemistry, spatial analysis, root growth, 410601 Land capability and soil productivity, 410605 Soil physics, 300403 Agronomy

Disciplines

Agriculture

Comments

This is WA field based research project between 2015 to 2023 and focused on the re-engineering of several soil types to examine root penetration and impact of crop yields. - Data and file overview: The data includes: Plant establishment and germination counts; Grain yield and harvest index (and yield components - physiological maturity, total weight, grain weight, head number); Soil compaction/depths; Soil water content, water infiltration, water repellence, water retention; Grain quality; Soil sampling (physical and chemical analyses); Pogo analysis; Crop canopy cover (Drone NVDI measurements)/temperature; Leaf chlorophyll content; Ploughing depth; Anthesis biomass and tiller counts; Assessment of amelioration efficacy. Paddock yield data, historical satellite imagery NVDI, assessment of yield potential. - Methodological information: Soil moisture was continuously measured hourly using an automated soil moisture sensor EnviroPro (Entelechy, Adelaide, SA). Root architecture for selected treatments was also imaged repeatedly in situ using clear glass tubes (Rhizo tubes, ICT International, Armidale, NSW) and a 360° scanner (CI-600, CID Bio-Science, Camas, Washington, USA). Soil profile samples were collected at 0–10, 10–20, 20–30, 30–40, 40–50, 50–60, 60–70 and 70–80 cm depths from each reengineering plot to measure soil physical and chemical properties. In some trials soil sample depths were shallower due to very hard subsoil especially for the control plots. When soil profiles samples were collected soil penetrometer resistance data were also measured using a digital penetrometer (CP40-II, Soil Measurement Systems, Tucson, AZ, USA). Soil was incubated in a controlled environment at field capacity for 6 months at 25oC day/night temperature and 80% of relative humidity. pH and EC were measured periodically up to 6 months. Boron was measured from a commercial laboratory (after 7 days only). Soil moisture was measured in a pressure plate chamber at different matric suctions. Sandy and clay soils were treated with 0 and 1 g/kg lime (neutralising value 93%). Treated subsoil was filled at 10-80 cm depth of PVC tubes (80 cm long, 15 cm diameter) and topsoil was used for 0-10 cm depth. Both soils were packed at 1.5 (low) and 1.7 (high) g/cm3 bulk densities. Plant samples were collected at flowering (Z65) for measuring nutrient uptake, shot biomass and root growth – with 4 replicates. Crops were mostly hand harvested except for the Northampton trial where a small plot harvester was used. Reengineered (decompacted and lime incorporated into 80 cm depth) Kurosol (an acidic duplex). Two rates or lime were applied with a factorial interaction with minus and plus clay treatments. There were3 replicates for each treatment. The plants were grown for 65 days on reconstructed soil columns (80 cm long, 15 cm diameter) in a screen house. Five farm businesses from the northern, central and southern wheatbelt were engaged to benchmark the amelioration they undertook on their properties. At least one paddock was targeted from each business with the criteria of available yield data before and after amelioration and long-term amelioration adoption on their farm. Satellite imagery from Landsat 8 or 5 captured at peak biomass (or as close to peak biomass as possible based on cloud cover/image quality) was sourced for each paddock and year. The imagery date was in first two weeks of September in most years/crops. The Normal Differential Vegetation Index (NDVI) was calculated for each season and averaged for at least three years pre and post amelioration at the Burracoppin and Geraldton sites. Yield raster and analysis were conducted in Ag Leader SMS Advanced (version 21.5) and QGIS 2.18. The PAT (Precision Agriculture Tools, developed by CSIRO) plug-in and Vesper (University of Sydney) were used to create the yield maps in QGIS. Yield maps were normalised for the years before and after amelioration and the coefficient of variation (CV) in Ag Leader SMS Advanced. Mean and standard deviation maps were created in ArcGIS version 10.4. The percent of estimated yield potential calculations were standardised for all case studies. Yield potential was estimated by the following equations in the different seasons and crop types. Wheat/barley yield potential (kg/ha) = (Available moisture – 110) * 20 Canola yield potential (kg/ha) = (Available moisture – 110) * 13 Lupins yield potential (kg/ha) = (Available moisture – 125) * 15 Available moisture = 0.25* Summer Rainfall November to March + Growing season rainfall April to October. Grain yield responses were collated into a database from soil amelioration research experiments and grower-scale trials. Yield responses to strategic deep tillage practices (ripping to various depths, ripping plus inclusion, spading, mouldboard plough, one-way disc plough, and combination plough plus ripping) were calculated as the difference from the untreated control treatment. Yield responses to amendments (fertiliser, N-rich biomass, chicken litter, clay, lime) were calculated as the difference from the deep tillage treatment (i.e., yield gain over and above the deep tillage practice alone). Constraints, including acidity, water repellence, soil strength, and poor water storage, were assessed based on soil measures. Laboratory analysis of samples collected at the field sites was used to determine soil water repellence and soil acidity. Soil acidity was assessed based on soil pH measurements in 0.01 M CaCl2 using soil collected in 100 mm increments down the soil profile, typically to a depth of 400–600 mm. Soil water repellence was measured using the molarity of ethanol droplet method for the top 50–100 mm of soil. Soil strength was assessed using cone penetrometer resistance when soil was at or near field capacity. A gap analysis gap analysis (SWOT analysis), involving cross functional experts from DPIRD and external organisations, was conducted to identify the strength, weaknesses, opportunities, and existing threats in currently available soil amelioration options. - Environmental/experimental conditions Further information from the trial-site each year include: Soil type; Weather conditions: (rainfall and temperature during the growing season); Nitrogen test results; - Data specific information: Summaries of these results are the GRDC and DPIRD websites

dc:access

Conditional

Funder

GRDC

Grant

DAW1902-003RTX

ROR of Contributing Organisation

https://ror.org/01awp2978

GRDC Project Code

DAW1902-003RTX

GRDC Project Title

Re-engineering soils to improve the access of crop root systems to water and nutrients stored in the subsoil

ORCID of each Author

0000-0002-3356-5425

Spatial Coverage

Albany, Badgin, Beaumont, Bencubbin, Binnu, Bokal, Bolgart, Bonnie Rock, Bonnie Vale, Borden, Boscabel, Burracoppin, Carnamah, Casuarinas, Eradu, Esperance, Geraldton, Goomalling, Grass Valley, Holt Rock, Kojaneerup, Kojonup, Lake Clifton, Meckering, Meenar, Moora, Northam, Nyabing, Ogilvie, Ongerup, Salmon Gums, South Burracoppin, Tardun, Tarin Rock, Toolbrunup, Wongan Hills, Western Australia

Observations Start Date

2015

Observations End Date

2023

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Digital Object Identifier (DOI)

https://doi.org/10.83199/qwy2-k853