Use of phenomics tools for decision support in the context of digital agronomy

Jose A. Jimenez-Berni1

1 Institute for Sustainable Agriculture, Spanish National Research Council (IAS-CSIC), Alameda del Obispo S/N, Cordoba, Spain, 14001, http://www.ias.csic.es/ , jose.jimenez.berni@csic.es  

Abstract

The use of high throughput plant phenotyping tools is becoming widespread in commercial breeding programs and pre-breeding research. Technologies such as LiDAR, multispectral and infrared thermography are now operated routinely onboard ground and aerial platform, providing imagery and data with unprecedented resolution. This data allows performing digital estimation of key physiological traits such as biomass, plant vigour, nutrition status, water stress or disease susceptibility. In modern gene-assisted selection, having this phenotyping capability is paramount to discover genetic markers and candidate genes of interest in the breeding programs.

However, the use of these phenotyping tools is still insignificant in agronomical applications. Even though agronomists have a long trajectory of adopting remote sensing technologies, such as satellite and aerial imagery or soil moisture monitoring, there is still a degree of frustration when it comes to interpreting the data obtained. Turning pretty pictures into valuable knowledge to assist agronomical decisions remains a challenge. The use of the phenomics paradigm, with a strong focus on the genetic and environmental interactions, together with new sensor technologies and tools that integrate crop simulation models, machine learning and big-data analysis, can alleviate some of these limitations, complementing existing remote sensing applications in making better informed agronomic decisions.

We present here different examples of how phenomics tools can be translated into an agronomic context, providing high throughput physiological measurements that reveal the effects of abiotic and biotic stresses in crops. The current limitations in terms of data processing, sensor calibration and confounding effects will be also discussed.

Key Words

Plant phenomics, LiDAR, infrared thermography, hyperspectral, decision support tools, machine learning.

Use of farmer data for closing yield and input gaps

Dr Patricio Grassini1

University of Nebraska -Lincoln

Abstract

Meeting demand for food, fiber, feed, and fuel in a world with 9.7 billion people by 2050 without negative environmental impact is the greatest scientific challenge facing humanity. The most common approach for evaluating yield-increasing practices involves conducting controlled research trials in which researchers experimentally evaluate various input levels or management practices to identify whether a particular input or practice improve yield, and if the degree of yield improvement justifies input costs. However, it is difficult and costly to run field experiments to evaluate each potential factor that might limit producer yields. Likewise, it is problematic to extrapolate results from these localized experiments to far-flung producer fields, especially if there is lack of an appropriate description of the biophysical environment (e.g., climate, soil) where these experiments are conducted. Finally, even with a large number of site-year experiments, management × environment (M × E) interactions are difficult to interpret without a rational understanding of what the word “environment” means beyond “site” and “year”. We argue here that having a database containing yield and management data from producer fields across multiple regions and years, properly contextualized relative to the biophysical environment, can be considered equivalent to running hundreds of field experiments to capture both major management effects and M x E interactions. Such analysis of large-scale producer data can provide a focus as to what treatments are the most promising to evaluate in more cost-effective agronomic field trial evaluations. We present here a novel approach that combines producer-reported data and a spatial framework to identify explanatory causes for yield variation over large geographic regions with diversity of climate, soils, and water regimes and we discussed how it can help inform and strategize research and extension programs at both local and regional levels.

The chickpea breeding challenge: tools and techniques to deliver varieties for an Australian pulse revolution

Kristy Hobson

1 New South Wales Department of Primary Industries, 4 Marsden Park Rd, Tamworth, NSW, 2340, kristy.hobson@dpi.nsw.gov.au

Abstract

Chickpea production in Australia has increased significantly over the past ten years peaking at 2 million tonnes in 2016 (ABARES). Whilst production largely came from the established chickpea production areas in the northern grain growing region, there has been increased grower interest from non-established or expansion areas in producing high value pulses. Improving the adaptation and reliability of chickpea in both the established and expansion areas provides a great opportunity and challenge to the Australian chickpea breeding community.

The availability of ‘modern breeding approaches’ such as high throughput and advanced phenotyping, rapid generation cycling, expanding genomic technologies, and the use of genomic selection all provide exciting capability to a plant breeder’s tool box. Prioritising and integrating the most relevant tools and technologies, and the ability to adapt plant breeding logistics are the major challenges to adopting these approaches in a variety development program. However the importance of understanding the biology of the crop and trait validation in the target environment still remains critical to the successful application of these approaches to improve crop genetic gain. The delivery of the improved variety and associated information, particularly in new production areas, is essential to ensure grain growers can fully exploit the variety and get a profitable impact in their farming system.

Crop simulation for farming systems: from phenotype to farm

Julianne Lilley

CSIRO Agriculture and Food, GPO Box 1700, Canberra ACT 2601, Australia

Abstract:

The value of simulation models to assist (i) crop breeding, (ii) agronomic research, and (iii) farm decision-making has been demonstrated in many studies (Holzworth et al. 2014; van Ittersum and Donatelli 2003). In the Australian grains industry several modelling platforms have been developed for a variety of needs, with APSIM (Agricultural Production Systems Simulator) the predominant tool (Robertson et al. 2015). APSIM allows models of crop and pasture production, residue decomposition, soil water and nutrient flow, and erosion to be configured to simulate soil and crop management for various production systems using conditional rules (Holzworth et al. 2014). The model has been well validated in many studies and shown to accurately capture the effects of variability in climate, soil type and management for a range of crops. Linking of APSIM and GRAZPLAN farming systems models (Moore et al. 2007) has also enabled assessment of whole farm issues associated with crop and animal production as well as environmental impacts of a range of practices on mixed farms (Lilley and Moore 2009, Robertson et al. 2009).

In this paper I discuss application of the APSIM model at three scales; (i) the value of individual genetic traits within the context of the farming system, (ii) single or multiple changes to management practices for individual crops over multiple years and locations, and (iii) the effect of a management or genotype change, in the context of multi-year sequences, or multiple paddocks across the whole farm. Case studies at each scale will demonstrate the value of simulation modelling as an integrated tool in modern farming systems research.

Host

The Australian Society of Agronomy is the professional body for agronomists in Australia. It has approximately 500 active members drawn from government, universities, research organisations and the private sector.

Photo Credits

David Marland Photography david_marland@hotmail.com Graham Centre for Agricultural Innovation, Charles Sturt University

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