Range Of Production Environments Research Articles

Sugarcane for water-limited environments: Enhanced capability of the APSIM sugarcane model for assessing traits for transpiration efficiency and root water supply

Genetic variation in traits affecting transpiration efficiency has been reported in sugarcane, but the impact of this variation on yield in a range of production environments needs to be estimated for assessing the priority and selection weightings to apply to these traits in crop improvement programs. A modelling approach may be useful and even necessary for reasonable assessment of these traits across production environments with different and temporally variable levels of water availability. Earlier theoretical modelling using the Agricultural Production Systems Simulator (APSIM)-Sugar module, found that traits affecting transpiration efficiency (TE) and water uptake by roots were important for improving sugarcane grown with highly variable rainfall. However, there were limitations of APSIM-Sugar in accommodating key physiological mechanisms, and this led to a revision of the APSIM-Sugar module described in this paper. Four key features were added to enable genetic variation in TE traits and root water supply (RWS) known to exist, to be modelled and assessed for predicted impact. These features were 1) the response of TE to water stress, 2) the midday flattening of hourly transpiration when plants are stressed, 3) conductance limits to hourly transpiration, which can apply even without stress and 4) the separation of soil hydraulic conductivity (k) and root length density (l) rather than the use of combined kl for determining RWS. A dataset of 182 observations of above ground biomass from 13 field experiments of sugarcane were used to check firstly that the new sugarcane module did not affect the simulation results when all the new features were disabled (model stability), secondly to check that the new features did not greatly reduce model performance, and thirdly, to determine the response or sensitivity of yields to the new features. Variation in parameter settings for the new features were based on the best evidence available for genetic variation in these traits, and were set before any testing against observations were made. With these features enabled in partial factorial combinations, the accuracy of the simulation of observed biomass, changed only to a minor extent compared to when no features were enabled. Separating k and l had the most consistent effect on improving model performance.Three additional published experiments with varying levels of imposed water stress were also simulated, with and without the new features. The simulation of biomass yield from two of the experiments was remarkably accurate regardless of which features were used in the simulation. Dry stalk yields reported for the third experiment were simulated accurately when no features were enabled and when midday flattening of transpiration was enabled and separate k and l enabled, one at a time. When a limit was placed on hourly transpiration, simulated TE and yield increased substantially when water was limiting but not in well-watered conditions; where yield was reduced.The new APSIM-Sugar features address the limitations of the original module (developed in 1999) for assessing water use related traits, including TE component traits and root growth. Effects of conductance on TE can now be simulated by limiting maximum hourly transpiration at the leaf level or k and specific root length at the root level. The new sugar module will also allow for better discrimination between sugarcane cultivars, now that vigour traits such leaf area development and radiation use efficiency are linked to root water uptake.

A global sensitivity analysis of cultivar trait parameters in a sugarcane growth model for contrasting production environments in Queensland, Australia

Abstract New sugarcane cultivars are continuously developed to improve sugar industry productivity. Despite this sugarcane crop models such as the ‘Sugar’ module in the A gricultural P roductions S ystem s IM ulator (APSIM-Sugar) have not been updated to reflect the most recent cultivars. The implications of misrepresenting cultivar parameters in APSIM-Sugar is difficult to judge as little research has been published on the likely values of these parameters and how uncertainty in parameter values may affect model outputs. A global sensitivity analysis can be used to better understand how cultivar parameters influence simulated yields. A Gaussian emulator was used to perform a global sensitivity analysis on simulated biomass and sucrose yield at harvest for two contrasting sugarcane-growing regions in Queensland, Australia. Biomass and sucrose yields were simulated for 42 years to identify inter-annual variability in output sensitivities to 10 parameters that represent physiological traits and can be used to simulated differences between sugarcane cultivars. Parameter main effect (S i ) and total effect (ST i ) sensitivity indices and emulator accuracy were calculated for all year-region-output combinations. When both regions were considered together parameters representing radiation use efficiency ( rue ), number of green leaves ( green_leaf_no ) and a conductance surrogate parameter ( k L ) were the most influential parameters for simulated biomass in APSIM-Sugar. Simulated sucrose yield was most sensitive to rue , sucrose_fraction (representing the fraction of biomass partitioned as sucrose in the stem) and green_leaf_no . However, climate and soil differences between regions changed the level of influence cultivar parameters had on simulation outputs. Specifically, model outputs were more sensitive to changes in the transp_eff_cf and k L parameters in the Burdekin region due to lower rainfall and poor simulated soil conditions. Collecting data on influential traits that are relatively simple to measure (e.g. number of green leaves) during cultivar development would greatly contribute to the simulation of new cultivars in crop models. Influential parameters that are difficult to measure directly such as transp_eff_cf and sucrose_fraction are ideal candidates for statistical calibration. Calibrating crop models either through direct observation or statistical calibration would allow crop modellers to better test how new cultivars will perform in a range of production environments.

Performance and Stability for Yield and Component Traits of Elite Sugarcane Genotypes Across Production Environments in Thailand

High and stable yield across varying environments are desirable traits of crop cultivars. The objectives of this study were to assess the performance and stability for cane yield, sugar yield and related component traits of elite sugarcane genotypes across the range of production environments in Thailand, and to identify the traits that contributed to high yield performance and stability. Data from multi-environment trials of ten sugarcane genotypes conducted at nine locations covering different sugarcane growing regions of the country for two crop-classes were used. Performance and stability of the test genotypes were evaluated by the GGE biplot method based on sugar yield, cane yield, commercial cane sugar (CCS), stalk number, stalk length, stalk weight and stalk diameter. Khon Kaen 3 and Kps94-13 were identified as the most superior genotypes for sugar yield, having consistent performance and stability of sugar yield across the two crop-classes and also ranking high in cane yield and CCS, while K88-92 was the most superior genotype in cane yield. The relative performance and stability of genotypes for plant crop and ratoon crop were consistent for all traits except stalk weight. Sugar yield was largely determined by cane yield, while stalk number and stalk length were the major factor determining the superiority of cane yield in the present study. Medium stalk diameter was also found to be associated with high CCS. These three traits could be used as indicators for selection for high and stable sugar yield in sugar cane.

Stability of Oleate Concentration in Soybean Lines Containing the High‐Oleate Transgene DP‐305423‐1

ABSTRACTSoybean [Glycine max (L.) Merr.] lines with the transgene DP‐305423‐1 produce oil with a high oleate concentration that is desirable for oxidative stability. It is important for commercial production to understand the oleate concentration that can be expected when cultivars with the transgene are grown over a range of environmental conditions. The objective of our study was to evaluate the environmental stability of oleate concentration for lines with the transgene in comparison to that of lines without the transgene. Three high‐oleate, normal‐linolenate (HONL); six high‐oleate, low‐linolenate (HOLL); three mid‐oleate, low‐linolenate (MOLL); three normal‐oleate, low‐linolenate (NOLL); and three normal‐oleate, normal‐linolenate (NONL) lines were evaluated across 18 environments during 2010 and 2011. The mean oleate concentration of all the lines ranged from 529 to 643 g kg−1 among the environments. The mean oleate concentration across environments was 842 g kg−1 for HONL, 826 g kg−1 for HOLL, 536 g kg−1 for MOLL, 262 g kg−1 for NOLL, and 238 g kg−1 for NONL lines. The range in oleate concentrations among environments averaged 73 g kg−1 for HONL, 128 g kg−1 for HONL, 148 g kg−1 for MOLL, 153 g kg−1 for NOLL, and 108 g kg−1 for NONL lines. The results indicated that the consistency in oleate concentrations across a range of production environments for cultivars with the DP‐305423‐1 transgene would be equal or superior to cultivars without the transgene.

Yield performance and adaptation of some Australian-grown rice varieties through multivariate analysis

Rice breeders are involved in developing new varieties for a diverse range of production environments to increase average yields. Interpretation of performance of several varieties evaluated in a broad range of environments is usually affected by variety × environment interactions. Application of multivariate analyses, especially multi-criteria decision making (MCDM) methods in variety × environment interaction studies can benefit interpretation of yield performance and adaptation of varieties in response to different environments. The case study discussed in this paper highlights the significant advantages of using MCDM methods to overcome constraints imposed by having to investigate a large number of variables inherent in such analysis. The grain yield of 13 rice varieties with varying characteristics, grown in 4 different regions in southern New South Wales (NSW), Australia, during the 2000–06 growing seasons was analysed using classification techniques and preference ranking organization method for enrichment evaluation (PROMETHEE) analysis. Significant variety × environment interaction was found to influence the yield performance of individual varieties across a range of environmental conditions. Classification of environments and geometrical analysis for interactive aid (GAIA) plot of PROMETHEE analysis identified the Murrumbidgee Irrigation Area (MIA) and Coleambally Irrigation Area (CIA) as higher yielding regions compared with the Eastern Murray Valley (EMV) and Western Murray Valley (WMV). Turbid water and cold weather conditions are suggested to explain the lower yield obtained in the WMV environment. In terms of varieties, Amaroo and Opus were identified as widely adaptable to most of the environments, while Jarrah was the least adaptable. Illabong can be considered as best adapted to the EMV or WMV regions, while Paragon and Reiziq can be regarded as best adapted to the MIA and CIA regions. Partial and complete ranking showed the interrelationships between the varieties for their yield performance and adaptation across all environments. Amaroo, Illabong, and Opus were ranked as the most preferred varieties, while Koshihikari, Kyeema, and Jarrah were ranked as the least preferred ones. Partial pre-order with 13 classes from PROMETHEE I analysis identified varieties with similar characteristics and aided the selection of suitable alternative varieties. The outcomes from the analyses reported here allow rice varieties or genotypes to be rated for yield stability for a specific or a range of different environments.

Plant‐to‐Plant Variability in Corn Production

Corn (Zea mays L.) grain yields are known to vary from plant to plant, but the extent of this variability across a range of environments has not been evaluated. This study was initiated to evaluate by‐plant corn grain yield variability over a range of production environments and to establish the relationships among mean grain yield, standard deviation, coefficient of variation, and yield range. A total of forty‐six 8‐ to 30‐m corn transects were harvested by plant in Argentina, Mexico, Iowa, Nebraska, Ohio, Virginia, and Oklahoma from 2002 to 2004. By‐plant corn grain yields were determined, and the average individual plant yields were calculated. Over all sites in all countries and states, plant‐to‐plant variation in corn grain yield averaged 2765 kg ha−1 (44.1 bu ac−1). At the sites with the highest average corn grain yield (11478 and 14383 kg ha−1, Parana Argentina, and Phillips, NE), average plant‐to‐plant variation in yield was 4211 kg ha−1 (67 bu ac−1) and 2926 kg ha−1 (47 bu ac−1), respectively. As average grain yields increased, so did the standard deviation of the yields obtained within each row. Furthermore, the yield range (maximum corn grain yield minus the minimum corn grain yield per row) was found to increase with increasing yield level. Regardless of yield level, plant‐to‐plant variability in corn grain yield can be expected and averaged more than 2765 kg ha−1 over sites and years. Averaging yield over distances >0.5 m removed the extreme by‐plant variability, and thus, the scale for treating other factors affecting yield should be less than 0.5 m. Methods that homogenize corn plant stands and emergence may decrease plant‐to‐plant variation and could lead to increased grain yields.

Amino acid composition of TopCross high-oil maize grain

AbstractIncreased amino acid content in high-oil maize (Zea mays L.) grain may add further value to its use in livestock rations, especially if this enhanced amino acid content is consistent across varying growing conditions. Most high-oil maize (HOM) grown in the USA utilizes the TopCross system which involves planting a blend (TC Blend) of two types of maize. Field experiments and on-farm studies were conducted in 1997 and 1998 to compare the amino acid profile of grain from HOM TC Blends with that of their normal-oil maize (NOM) counterparts across a range of production environments in Ohio. In 1997, the composition of four amino acids (lysine, methionine, glycine and arginine) was significantly higher in HOM compared to NOM grain. In 1998, nine amino acids (lysine, methionine, glycine, arginine, asparagine, threonine, serine, cysteine and tryptophan) were greater in HOM than in NOM grain. Lysine and methionine content in HOM grain averaged 12 and 13% higher than in NOM grain in both years. The number of amino acids significantly affected by the grain parent was greater than that for maize type each year. A significant maize type × grain parent interaction for a limited number of amino acids suggest that TC Blend grain parents may affect the consistency of amino acid composition in HOM grain. Results of this study demonstrate that the levels of several amino acids, including economically important lysine and methionine, were consistently greater in HOM than in NOM grain across a range of production environments. Modelling with livestock ration balancing software showed that the additional amino acids and oil in HOM added 12–20% to its value as livestock feed.

TopCross High Oil Corn Production

High oil corn (Zea mays L.) enhances the feed rations of livestock and poultry and reduces the need for expensive dietary supplements. The TopCross1 grain production system, which involves use of a blend (TC Blend) of two types of corn, is rapidly gaining popularity as the preferred method of producing high oil corn (HOC). Field experiments and on‐farm studies were performed in 1995 to 1999 across a range of production environments in Ohio to compare select grain quality attributes of HOC TC Blends with their conventional counterparts (check hybrids). Oil levels in grain were 31 g kg−1 higher in TC Blends compared with check hybrids averaged across experiments and on‐farm tests. Drought conditions and late plantings had little or no effect on the differences in oil levels between TC Blends and check hybrids. Protein levels were generally similar for TC Blends and check hybrids, but lysine content was 20% greater in grain of TC Blends. Metabolizable energy levels in grain averaged 130 kcal kg−1 more in TC Blends compared with check hybrids. Starch content was five percent less in TC Blends compared with checks. The fatty acid composition of grain from TC Blends was higher for stearic acid (2.5 vs. 2.0%) and oleic acid (34.6 vs. 25.9%), but lower for linoleic acid (49.5 vs. 58.8%) and linolenic (1.0 vs. 1.3%), compared with check hybrids. Results of this study demonstrate that TC Blends can produce grain with consistently higher nutritional levels than conventional corn hybrids under variable growing conditions.

TopCross High‐Oil Corn Production

The TopCross grain production system is rapidly gaining popularity as the preferred method of producing high‐oil corn (Zea mays L.). A blend (TC Blend) of two types of corn is planted to produce TopCross high‐oil corn (HOC) grain. Limited information is available on the effects of the TopCross system on agronomic traits that may determine the profitability of HOC production. Field experiments and on‐farm studies were performed in 1995 to 1999 across a range of production environments in Ohio to compare the agronomic performance of TC Blends with their conventional counterparts (check hybrids). Grain yields of TC Blends averaged across experiments and on‐farm studies were 8% less than those of check hybrids. The TC Blends were as tolerant to drought conditions as the check hybrids. Stalk lodging and barrenness were comparable for TC Blends and check hybrids. Little evidence existed that kernel set in TC Blends was reduced by inadequate pollen availability due to the limited number of pollinator plants in the blend. Factors that may contribute to the differences in grain yields between TC Blends and check hybrids included lower plant populations in TC Blends at harvest, competition between the two components of the blend (TC Blend pollinators and male sterile grain parents), and the physiological cost of oil synthesis. The lower grain yield, higher grain moisture content, and lower test weight associated with TC Blends should be considered when determining TopCross HOC production costs, especially if HOC grain is being produced under contract.

Development pathways toward sustainable systems following slash-and-burn

Alternative solutions to the loss of production potential and to the increasing rural poverty engendered by many slash-and-burn systems require major shifts in the evolution toward greater sustainability. Slash-and-burn practices are the starting point for several evolutionary pathways in agricultural development. The changes cannot be single-factor (such as increasing fertilizer inputs or use of better crop varieties). The transformation of highly complex systems requires substitution of enterprises that are more productive in geographical areas that may be marginal for food and feedgrains. These new systems often require markets for the wide variety of high-value crop and animal products that are most appropriate in highly diverse systems adapted to a range of production environments. The best existing examples of such systems indicate that potential gains in productivity are enormous. The structuring of several systems to enhance biological interaction can increase production efficiencies. Stimuli for change will involve social, political, and economic dimensions. It is essential for effective change in most systems that the planning horizon for farmer investment of both labor and capital be extended to at least 10 years. Several specific examples are presented.