Breeding for low input conditions and consequences for participatory plant breeding: Examples from tropical maize and wheat

Participatory plant breeding (PPB) has been suggested as an effective alternative to formal plant breeding (FPB) as a breeding strategy for achieving productivity gains under low input conditions. With genetic progress through PPB and FPB being determined by the same genetic variables, the likelihood of success of PPB approaches applied in low input target conditions was analyzed using two case studies from FPB that have resulted in significant productivity gains under low input conditions: (1) breeding tropical maize for low input conditions by CIMMYT, and (2) breeding of spring wheat for the highly variable low input rainfed farming systems in Australia. In both cases, genetic improvement was an outcome of long-term investment in a sustained research effort aimed at understanding the detail of the important environmental constraints to productivity and the plant requirements for improved adaptation to the identified constraints, followed up by the design and continued evaluation of efficient breeding strategies. The breeding strategies used differed between the two case studies but were consistent in their attention to the key determinants of response to selection: (1) ensuring adequate sources of genetic variation and high selection pressures for the important traits at all stages of the breeding program, (2) use of experimental procedures to achieve high levels of heritability in the breeding trials, and (3) testing strategies that achieved a high genetic correlation between performance of germplasm in the breeding trials and under on-farm conditions. The implications of the outcomes from these FPB case studies for realizing the positive motivations for adopting PPB strategies are discussed with particular reference for low input target environment conditions.

A multifunctional polyketide-peptide synthetase essential for albicidin biosynthesis in Xanthomonas albilineans

Albicidins, a family of potent antibiotics and phytotoxins produced by the sugarcane leaf scald pathogen Xanthomonas albilineans, inhibit DNA replication in bacteria and plastids. A gene located by Tn5-tagging was confirmed by complementation to participate in albicidin biosynthesis. The gene (xabB) encodes a large protein (predicted Mr 525695), with a modular architecture indicative of a multifunctional polyketide synthase (PKS) linked to a non-ribosomal peptide synthetase (NRPS). At 4801 amino acids in length, XabB is the largest reported PKS–NRPS. Twelve catalytic domains in this multifunctional enzyme are arranged in the order N terminus–acyl-CoA ligase (AL)–acyl carrier protein (ACP)–ß-ketoacyl synthase (KS)–ß-ketoacyl reductase (KR)–ACP–ACP–KS–peptidyl carrier protein (PCP)–condensation (C)–adenylation–PCP–C. The modular architecture of XabB indicates likely steps in albicidin biosynthesis and approaches to enhance antibiotic yield. The novel pattern of domains, in comparison with known PKS–NRPS enzymes for antibiotic production, also contributes to the knowledge base for rational design of enzymes producing novel antibiotics.

Differentiation of peanut seedborne potyviruses and curcumoviruses by RT-PCR

Seedborne peanut viruses pose important constraints to peanut production and safe movement of germ plasm. They also pose a risk of accidental introduction into previously disease-free regions. We have developed reverse transcription-polymerase chain reaction (RT-PCR) assays based on identical cycling parameters which identified peanut stripe, Peanut mottle, Peanut stunt, and Cucumber mosaic viruses through production of specific DNA fragments of 234 bp, 327 bp, 390 bp, and 133 bp, respectively. Assay sensitivity in the picogram range was achieved. The two potyviruses and two cucumoviruses could be differentiated using duplex RT-PCR assays. These assays should be useful for testing peanut leaves or seeds for virus identification in epidemiological studies, seed testing or in post-entry quarantine.

Advances in application of climate prediction in agriculture

Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems. In this paper, we outline the basis for climate prediction, with emphasis on the El Nino-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction. In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary Knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based oil simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction. We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential. (C) 2001 Elsevier Science Ltd. All rights reserved.

Increasing profits and reducing risks in crop production using participatory systems simulation approaches

The development of cropping systems simulation capabilities world-wide combined with easy access to powerful computing has resulted in a plethora of agricultural models and consequently, model applications. Nonetheless, the scientific credibility of such applications and their relevance to farming practice is still being questioned. Our objective in this paper is to highlight some of the model applications from which benefits for farmers were or could be obtained via changed agricultural practice or policy. Changed on-farm practice due to the direct contribution of modelling, while keenly sought after, may in some cases be less achievable than a contribution via agricultural policies. This paper is intended to give some guidance for future model applications. It is not a comprehensive review of model applications, nor is it intended to discuss modelling in the context of social science or extension policy. Rather, we take snapshots around the globe to 'take stock' and to demonstrate that well-defined financial and environmental benefits can be obtained on-farm from the use of models. We highlight the importance of 'relevance' and hence the importance of true partnerships between all stakeholders (farmer, scientists, advisers) for the successful development and adoption of simulation approaches. Specifically, we address some key points that are essential for successful model applications such as: (1) issues to be addressed must be neither trivial nor obvious; (2) a modelling approach must reduce complexity rather than proliferate choices in order to aid the decision-making process (3) the cropping systems must be sufficiently flexible to allow management interventions based on insights gained from models. The pro and cons of normative approaches (e.g. decision support software that can reach a wide audience quickly but are often poorly contextualized for any individual client) versus model applications within the context of an individual client's situation will also be discussed. We suggest that a tandem approach is necessary whereby the latter is used in the early stages of model application for confidence building amongst client groups. This paper focuses on five specific regions that differ fundamentally in terms of environment and socio-economic structure and hence in their requirements for successful model applications. Specifically, we will give examples from Australia and South America (high climatic variability, large areas, low input, technologically advanced); Africa (high climatic variability, small areas, low input, subsistence agriculture); India (high climatic variability, small areas, medium level inputs, technologically progressing; and Europe (relatively low climatic variability, small areas, high input, technologically advanced). The contrast between Australia and Europe will further demonstrate how successful model applications are strongly influenced by the policy framework within which producers operate. We suggest that this might eventually lead to better adoption of fully integrated systems approaches and result in the development of resilient farming systems that are in tune with current climatic conditions and are adaptable to biophysical and socioeconomic variability and change. (C) 2001 Elsevier Science Ltd. All rights reserved.

An embryo-rescue protocol for Vigna interspecific hybrids

A successful embryo-rescue and culture protocol was developed for use with several indigenous Vigna species and mungbean cultivars grown in Australia. Germination of Vigna immature embryos and their subsequent development into plants was influenced by the time at which the embryos were isolated and by which medium additives were placed in the embryo-rescue medium. A medium containing MS basal nutrients with sucrose (88 mM), casein hydrolysate (500 mg L-1) and agar (8 g L-1) but devoid of plant-growth regulators was found to be the best for germination of immature embryos for all four Vigna species investigated. The protocol for successful germination of non-hybrid immature embryos was applied to the recovery of interspecific hybrids involving mungbean and five native Vigna species that had previously been found difficult to hybridise. Several putative hybrid plants were obtained including a confirmed interspecific cross between V. luteola (Jacq.) Benth and V. marina (Burm.) Merrill.

Future contributions of crop modelling - from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement

Crop modelling has evolved over the last 30 or so years in concert with advances in crop physiology, crop ecology and computing technology. Having reached a respectable degree of acceptance, it is appropriate to review briefly the course of developments in crop modelling and to project what might be major contributions of crop modelling in the future. Two major opportunities are envisioned for increased modelling activity in the future. One opportunity is in a continuing central, heuristic role to support scientific investigation, to facilitate decision making by crop managers, and to aid in education. Heuristic activities will also extend to the broader system-level issues of environmental and ecological aspects of crop production. The second opportunity is projected as a prime contributor in understanding and advancing the genetic regulation of plant performance and plant improvement. Physiological dissection and modelling of traits provides an avenue by which crop modelling could contribute to enhancing integration of molecular genetic technologies in crop improvement. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

The E(NK) model: extending the NK model to incorporate gene-by-environment interactions and epistasis for diploid genomes

The haploid NK model developed by Kauffman can be extended to diploid genomes and to incorporate gene-by-environment interaction effects in combination with epistasis. To provide the flexibility to include a wide range of forms of gene-by-environment interactions, a target population of environment types (TPE) is defined. The TPE consists of a set of E different environment types, each with their own frequency of occurrence. Each environment type conditions a different NK gene network structure or series of gene effects for a given network structure, providing the framework for defining gene-by-environment interactions. Thus, different NK models can be partially or completely nested within the E environment types of a TPE, giving rise to the E(NK) model for a biological system. With this model it is possible to examine how populations of genotypes evolve in context with properties of the environment that influence the contributions of genes to the fitness values of genotypes. We are using the E(NK) model to investigate how both epistasis and gene-by-environment interactions influence the genetic improvement of quantitative traits by plant breeding strategies applied to agricultural systems. © 2002 Wiley Periodicals, Inc.

The GP problem: quantifying gene-to-phenotype relationships

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.

Genetic shifts in Helicoverpa armigera Hubner (Lepidoptera : Noctuidae) over a year in the Dawson/Callide Valleys

Microsatellites were used to analyse 68 collections of Helicoverpa armigera in the Dawson/Callide Valleys in central Queensland. The study aimed to evaluate the genetic structure in this region over a 12-month period (September 2000-August 2001). The results detected genetic shifts in H. armigera collections, with genetic changes occurring month by month. Collections in any month were genetically distant from the preceding month's collections. There was no observed difference between collections of H. armigera from the Biloela region and those found in the Theodore region of central Queensland. The data support the current area-wide management strategies for H. armigera by reinforcing the importance and contribution of local management practices. The study also indicates a need for the continuation of regional or Australia-wide approaches to management of the low levels of immigration that are occurring, and for future high pest pressure years.

Low input tillage/cropping systems for limited resource areas

Agriculture in limited resource areas is characterized by small farms which an generally too small to adequately support the needs of an average farm family. The farming operation can be described as a low input cropping system with the main energy source being manual labor, draught animals and in some areas hand tractors. These farming systems are the most important contributor to the national economy of many developing countries. The role of tillage is similar in dryland agricultural systems in both the high input (HICS) and low input cropping systems (LICS), however, wet cultivation or puddling is unique to lowland rice-based systems in low input cropping systems. Evidence suggest that tillage may result in marginal increases in crop yield in the short term, however, in the longer term it may be neutral or give rise to yield decreases associated with soil structural degradation. On marginal soils, tillage may be required to prepare suitable seedbeds or to release adequate Nitrogen through mineralization, but in the longer term, however, tillage reduces soil organic matter content, increases soil erodibility and the emission of greenhouse gases. Tillage in low input cropping systems involves a very large proportion of the population and any changes: in current practices such as increased mechanization will have a large social impact such as increased unemployment and increasing feminization of poverty, as mechanization may actually reduce jobs for women. Rapid mechanization is likely to result in failures, but slower change, accompanied by measures to provide alternative rural employment, might be beneficial. Agriculture in limited resource areas must produce the food and fiber needs of their community, and its future depends on the development of sustainable tillage/cropping systems that are suitable for the soil and climatic conditions. These should be based on sound biophysical principles and meet the needs of and he acceptable to the farming communities. Some of the principle requirements for a sustainable system includes the maintenance of soil health, an increase in the rain water use efficiency of the system, increased use of fertilizer and the prevention of erosion. The maintenance of crop residues on the surface is paramount for meeting these requirements, and the competing use of crop residues must be met from other sources. These requirements can be met within a zonal tillage system combined with suitable agroforestry, which will reduce the need for crop residues. It is, however, essential that farmers participate in the development of any new technologies to ensure adoption of the new system. (C) 2001 Elsevier Science B.V. All rights reserved.