Annual nitrogen dynamics and urea fertilizer recoveries from a dairy pasture using 15N; effect of nitrification inhibitor DMPP and reduced application rates

Direct nitrogen (N) losses from pastures contribute to the poor nitrogen use efficiency of the dairy industry, though the exact fate of applied N and the processes involved are largely unknown. Nitrification inhibitors such as DMPP can potentially increase fertilizer N use efficiency (NUE), though few studies globally have examined the effectiveness of DMPP coated urea in pastures. This study quantified the NUE of DMPP combined with reduced application rates, and the effect on N dynamics and plant–soil interactions over an annual ryegrass/kikuyu rotation in Queensland, Australia. Labeled 15N urea and DMPP was applied over 7 winter applications at standard farmer (45 kg N ha−1) and half (23 kg N ha−1) rates. Fertilizer recoveries and NUE were calculated over 13 harvests, and the contribution of fertilizer and soil N estimated. Up to 85% of the annual N harvested was from soil organic matter. DMPP at the lower rate increased annual yields by 31% compared to the equivalent urea treatment with no difference to the high N rates. Almost 40% of the N added at the conventional fertilizer application rate as urea was lost to the environment; 80 kg N ha−1 higher than the low DMPP. Combining the nitrification inhibitor DMPP with reduced fertilizer application rates shows substantial potential to reduce N losses to the environment while sustaining productivity in subtropical dairy pastures.

Legumes or nitrification inhibitors to reduce {N2O} emissions from subtropical cereal cropping systems in Oxisols?

The DAYCENT biogeochemical model was used to investigate how the use of fertilizers coated with nitrification inhibitors and the introduction of legumes in the crop rotation can affect subtropical cereal production and {N2O} emissions. The model was validated using comprehensive multi-seasonal, high-frequency dataset from two field investigations conducted on an Oxisol, which is the most common soil type in subtropical regions. Different N fertilizer rates were tested for each N management strategy and simulated under varying weather conditions. DAYCENT was able to reliably predict soil N dynamics, seasonal {N2O} emissions and crop production, although some discrepancies were observed in the treatments with low or no added N inputs and in the simulation of daily {N2O} fluxes. Simulations highlighted that the high clay content and the relatively low C levels of the Oxisol analyzed in this study limit the chances for significant amounts of N to be lost via deep leaching or denitrification. The application of urea coated with a nitrification inhibitor was the most effective strategy to minimize {N2O} emissions. This strategy however did not increase yields since the nitrification inhibitor did not substantially decrease overall N losses compared to conventional urea. Simulations indicated that replacing part of crop N requirements with N mineralized by legume residues is the most effective strategy to reduce {N2O} emissions and support cereal productivity. The results of this study show that legumes have significant potential to enhance the sustainable and profitable intensification of subtropical cereal cropping systems in Oxisols.

Non-linear response of soil N2O emissions to nitrogen fertiliser in a cotton-fallow rotation in sub-tropical Australia

Nitrogen fertiliser is a major source of atmospheric N2O and over recent years there is growing evidence for a non-linear, exponential relationship between N fertiliser application rate and N2O emissions. However, there is still high uncertainty around the relationship of N fertiliser rate and N2O emissions for many cropping systems. We conducted year-round measurements of N2O emission and lint yield in four N rate treatments (0, 90, 180 and 270 kg N ha-1) in a cotton-fallow rotation on a black vertosol in Australia. We observed a nonlinear exponential response of N2O emissions to increasing N fertiliser rates with cumulative annual N2O emissions of 0.55 kg N ha-1, 0.67kg N ha-1, 1.07 kg N ha-1 and 1.89 kg N ha-1 for the four respective N fertiliser rates while no N response to yield occurred above 180N. The N fertiliser induced annual N2O EF factors increased from 0.13% to 0.29% and 0.50% for the 90N, 180N and 270N treatments respectively, significantly lower than the IPCC Tier 1 default value (1.0 %). This non-linear response suggests that an exponential N2O emissions model may be more appropriate for use in estimating emission of N2O from soils cultivated to cotton in Australia. It also demonstrates that improved agricultural N management practices can be adopted in cotton to substantially reduce N2O emissions without affecting yield potential.

Agronomy looking forward, thinking broadly.

Global trends in human population and agriculture dictate that future calls made on the resources (physical, human, financial) and systems involved in producing food will be increasingly more demanding and complex. Both plant breeding and improved agronomy lift the potential yield of crops, a key component in progressing farm yield, so society can reasonably expect both agronomy as a science and agronomists as practitioners to contribute to the successful delivery of necessary change. By reflecting on current trends in agricultural production (diversification, intensification, integration, industrialisation, automation) and deconstructing a futuristic scenario of attempting agricultural production on Mars, it seems the skills agronomists will require involve not only the mandatory elements of their discipline but also additional skills that enable engagement with, even leadership of, teams who integrate (in sum or part) engineering, (agri-)business, economics and operational management, and build the social capital required to create and maintain a diverse array of enhanced and new ethical production systems and achieve increasing efficiencies within them.

Diagnostic Agronomy

Develop and implement a diagnostic framework designed to help advisors and farmers identify the causes of poor crop performance and implement appropriate remedial measures.

Strategic tillage in no-till farming systems in Australia’s northern grains-growing regions: II. Implications for agronomy, soil and environment

In semi-arid sub-tropical areas, a number of studies concerning no-till (NT) farming systems have demonstrated advantages in economic, environmental and soil quality aspects over conventional tillage (CT). However, adoption of continuous NT has contributed to the build-up of herbicide resistant weed populations, increased incidence of soil- and stubble-borne diseases, and stratification of nutrients and organic carbon near the soil surface. Some farmers often resort to an occasional strategic tillage (ST) to manage these problems of NT systems. However, farmers who practice strict NT systems are concerned that even one-time tillage may undo positive soil condition benefits of NT farming systems. We reviewed the pros and cons of the use of occasional ST in NT farming systems. Impacts of occasional ST on agronomy, soil and environment are site-specific and depend on many interacting soil, climatic and management conditions. Most studies conducted in North America and Europe suggest that introducing occasional ST in continuous NT farming systems could improve productivity and profitability in the short term; however in the long-term, the impact is negligible or may be negative. The short term impacts immediately following occasional ST on soil and environment include reduced protective cover, soil loss by erosion, increased runoff, loss of C and water, and reduced microbial activity with little or no detrimental impact in the long-term. A potential negative effect immediately following ST would be reduced plant available water which may result in unreliability of crop sowing in variable seasons. The occurrence of rainfall between the ST and sowing or immediately after the sowing is necessary to replenish soil water lost from the seed zone. Timing of ST is likely to be critical and must be balanced with optimising soil water prior to seeding. The impact of occasional ST varies with the tillage implement used; for example, inversion tillage using mouldboard tillage results in greater impacts as compared to chisel or disc. Opportunities for future research on occasional ST with the most commonly used implements such as tine and/or disc in Australia’s northern grains-growing region are presented in the context of agronomy, soil and the environment.

The effects of growth environment and agronomy on grain quality

Achieving quality requires the selection of varieties suited to prevailing environments and cropping systems. For well-adapted varieties, yield and quality can still be affected strongly by the weather and by agronomic interventions. Some of the strongest influences are heat and drought during grain filling, the availability of nitrogen and sulphur, the control of leaf and ear diseases, and the control of lodging. The effects of these and other factors are described, particularly in relation to the ‘point of sale measures’ for wheat grain.

Ag Engineering and Agronomy Farm and Central Iowa Research Farms Summary

Includes Ag Engineering and Agronomy Farm Farm and Weather Summary, Central Iowa Farms Farm and Weather Summary and Project Lists

Handbook of Ohio experiments in agronomy.

Mode of access: Internet.

Journal of the American Society of Agronomy.

Title from cover.

Banana agronomy – can unravelling the Musa genome help?

Unravelling the Musa genome allows genes and alleles linked to desired traits to be identified. Short stature and early flowering are desirable agronomic features of banana, as they are of bread wheat (Triticum aestivum). In wheat they were achieved through knowledge of the physiology and genetics of vernalization and photoperiod during development. Bananas and plantains have a facultative long-day response to photoperiod, as do wheat and wall cress (Arabidopsis thaliana). Using keyword searches of the genome of Musa acuminata 'Pahang' we found homologues of the genes of either T. aestivum or Arabidopsis that govern responses to vernalization and photoperiod. This knowledge needs to be interpreted in the context of plant development. Bananas have juvenile, mid-vegetative and reproductive phases of development. Leaf and bunch 'clocks' operate concurrently throughout the juvenile and mid-vegetative phases. In the mid-vegetative phase the plant becomes sensitive to photoperiod. Increased sensitivity to photoperiod reduces the overall pace of the bunch clock without affecting the leaf clock. Separation of the clocks changes the link between leaf number and time of flowering. The 'critical' quantitative trait for the time of flowering is the pace of the bunch clock up to bunch initiation. For bunch size it is the duration of the subsequent phase of female hand formation. Plants with either a short juvenile phase or a faster bunch clock in the mid-vegetative phase will produce fewer leaves and bunch early. In turn, independent manipulation of hand number per bunch and/or fruit per hand will provide manageable bunches with appropriate fruit size. Using published data we explore relationships between plant height, leaf number, bunch weight and hand number among bananas and plantains. Identifying and then manipulating the appropriate genes in Musa opens opportunities for earlier flowering, leading to plants with desirable agronomic qualities.

Sorghum Crop Modeling and Its Utility in Agronomy and Breeding

Crop models are simplified mathematical representations of the interacting biological and environmental components of the dynamic soil–plant–environment system. Sorghum crop modeling has evolved in parallel with crop modeling capability in general, since its origins in the 1960s and 1970s. Here we briefly review the trajectory in sorghum crop modeling leading to the development of advanced models. We then (i) overview the structure and function of the sorghum model in the Agricultural Production System sIMulator (APSIM) to exemplify advanced modeling concepts that suit both agronomic and breeding applications, (ii) review an example of use of sorghum modeling in supporting agronomic management decisions, (iii) review an example of the use of sorghum modeling in plant breeding, and (iv) consider implications for future roles of sorghum crop modeling. Modeling and simulation provide an avenue to explore consequences of crop management decision options in situations confronted with risks associated with seasonal climate uncertainties. Here we consider the possibility of manipulating planting configuration and density in sorghum as a means to manipulate the productivity–risk trade-off. A simulation analysis of decision options is presented and avenues for its use with decision-makers discussed. Modeling and simulation also provide opportunities to improve breeding efficiency by either dissecting complex traits to more amenable targets for genetics and breeding, or by trait evaluation via phenotypic prediction in target production regions to help prioritize effort and assess breeding strategies. Here we consider studies on the stay-green trait in sorghum, which confers yield advantage in water-limited situations, to exemplify both aspects. The possible future roles of sorghum modeling in agronomy and breeding are discussed as are opportunities related to their synergistic interaction. The potential to add significant value to the revolution in plant breeding associated with genomic technologies is identified as the new modeling frontier.