Posts Tagged ‘Agronomy journal’

Analysis and interpretation of interactions in agricultural research

Posted by Carelia Juarez on , in Journal Articles

Published in Agronomy Journal, 2014

Vargas, M.; Glaz, B.; Alvarado, G.; Pietragalla, J.; Morgounov, A.I.; Zelenskiy, Y.; Crossa, J.

When reporting on well-conducted research, a characteristic of a complete and proper manuscript is one that includes analyses and interpretations of all interactions. Our purpose is to show how to analyze and interpret interactions in agronomy and breeding research by means of three data sets comprising random and fixed effects. Experiment 1 tested wheat (Triticum aestivum L.) at two N and four P fertilizer rates in two soil types. For this data set, we used a fixed-effect linear model with the highest order (three-way) interaction considered first and then worked down through the lower order interactions and main effects to illustrate the importance of interactions in data analysis. Experiment 2 evaluated maize (Zea mays L.) hybrids under four rates of N for 3 yr. For this data set, we used a linear mixed model and partitioned the four N rates into orthogonal polynomials. Experiment 3 evaluated genotypes in six environments where the objective was to show how to study genotype × environment interactions. Researchers must analyze all interactions, determine if they are due to changes in rank (crossover) or only to changes in scale, and then judge whether reporting on significant main effects or interactions would best explain the biological responses in their experiments. In an experiment with more than one factor, complete and correct analysis of interactions is essential for reporting and interpreting the research properly.


Dry soil planting of sorghum for vertisols of Ethiopia

Posted by Carelia Juarez on , in Journal Articles

Published in Agronomy Journal 106 (2) : 469-474, 2014

Merga, F.Kindie Tesfaye FantayeWortmann, C.S.

Soil water deficits constrain productivity in Ethiopia. Farmers respond to variable onset of rain in the Central Rift Valley (CRV) of Ethiopia by dry soil planting sorghum [Sorghum bicolor (L.) Moench] to take advantage of early rains and increase the period of crop growth before rains cease in late September or early October. Crop establishment is often unsatisfactory. The effect of dry soil planting depth for sorghum was evaluated with three water deficit scenarios on Vertisols in CRV. Dry soil planting at 5-cm depth resulted in relatively better seedling emergence, plant survival, individual plant wt., and leaf plant–1 for all water regimes as compared with other dry planting depths. The best plant establishment (80%) occurred with a local variety planted at 5-cm depth with no water applied for 15 d after dry soil planting followed by 30 mm applied at 5-d intervals from 15 to 30 d after planting (W3). The worst establishment (12%) was with planting at 7-cm depth and irrigating after planting with 30 mm of water and then adding 30 mm at 5-d intervals from 15 to 30 d after planting (W1). Risk of failed crop establishment with dry soil planting on a Vertisol is less with 5 cm compared with other planting depths. The W3 type of water deficit, with seed lying in dry soil for 15 d before water was applied, is less detrimental to sorghum establishment and early growth, compared with rainfall after planting followed by a dry period of 15 d.

Evaluation and interpretation of interactions

Posted by Carelia Juarez on , in Journal Articles

Published in Agronomy Journal, 2013

Jose Crossa , Mateo Vargas, C. Mariano Cossani, Gregorio Alvarado, Juan Burgueño, Ky L. Mathews and Matthew P. Reynolds

Understanding the factors that define a given interaction is important in agricultural, agronomic, and plant breeding research, where agronomic treatments or genotypes are evaluated under several environmental conditions and where interactions usually complicate a researcher’s decisions. We give examples of how interactions, in common agricultural experiments, can be examined and studied to make use of the rich information available on the interaction term of the model. Examples with different levels of interaction complexity are used to illustrate how to analyze and interpret interactions and how interaction components can be partitioned into comparisons with sensible biological interpretations. It will offer researchers a greater understanding of how to exploit interaction information beyond the standard statistical tests performed in the usual analysis of variance. Simple SAS codes for performing standard interaction contrasts and defining interaction covariables are provided.

Potassium Fertilization in Rice-Wheat System across Northern India: Crop Performance and Soil Nutrients

Posted by Carelia Juarez on , in Journal Articles

Published in Agronomy Journal 105 (2) : 1-11, 2013

Vinod K. Singh, Brahma S. Dwivedi, Roland J. Buresh, M. L. Jat, Kaushik Majumdar, Babooji Gangwar, Vidhi Govil, and Susheel K. Singh

Rice (Oryza sativa L.)–wheat (Triticum aestivum L.) cropping in South Asia is under stress due to widespread removal of plant nutrients in excess of their application. We evaluated K, S, and Zn application to rice and wheat in 60 farmer’s fields in five districts across northern India. We compared the existing farmer’s fertilizer practice (FFP), which in most cases did not include application of K, S, or Zn, with application of K only, S + Zn, or K + S + Zn. Application of K increased rice yields by 0.6 to 1.2 Mg ha–1 and wheat yields by 0.2 to 0.7 Mg ha–1 across the locations varying in soil texture, soil K, climate, and irrigation. Application of S and Zn with K further increased yields. Added net return from fertilization with only K, as compared to FFP, ranged from U.S.$ 114 to 233 ha–1 for rice and U.S.$ 29 to 214 ha–1 for wheat. Added net return further increased when S and Zn were combined with K. Total plant K per unit of grain yield was comparable for mature rice and wheat (22 kg Mg grain–1). Soil exchangeable and non-exchangeable K decreased without K application during one rice–wheat cropping cycle. Rice and wheat yields increased with application of K across the range in exchangeable soil K from 60 to 162 mg kg–1. Approaches are needed to reliably predict fertilizer K requirements when crops respond relatively uniformly to K across a wide range in exchangeable K.


META: A Suite of SAS Programs to Analyze Multienvironment Breeding Trials

Posted by Carelia Juarez on , in Journal Articles

Published in Agronomy Journal 105 (1) : 11-19, 2012

Mateo Vargas, Emily Combs, Gregorio Alvarado, Gary Atlin, Ky Mathewsc and José Crossa

Multienvironment trials (METs) enable the evaluation of the same genotypes under a variety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 33 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. The entire program is run through a graphical user interface. The program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates best linear unbiased estimators (BLUEs) and best linear unbiased predictors (BLUPs) for the main response variable and BLUEs for all other traits. For all traits, it calculates variance components by restricted maximum likelihood, least significant difference, coefficient of variation, and broad-sense heritability using PROC MIXED. The program can analyze each location separately, combine the analysis by management conditions, or combine all locations. The flexibility and simplicity of use of this program makes it a valuable tool for analyzing METs in breeding and agronomy. The META program can be used by any researcher who knows only a few fundamental principles of SAS.


Rainfall as a limiting factor for wheat grain yield in permanent raised-beds

Posted by Carelia Juarez on , in Journal Articles

Published in Agronomy Journal  104 (4) : 1171-1175, 2012

Agustin Limon-Ortega and Ken Sayre

The planting system on permanent raised-beds for rainfed wheat (Triticum aestivum L.) production in the central highlands of Mexico is an option that needs to be documented for moderate-yield environments. Long-term plots were established under this technology in the 2002 crop season. The objective was to evaluate wheat grain yield performance as related to rainfall and soil characteristics until 2009. The experiment was conducted in rotation with maize (Zea mays L.) and monoculture. Four N rates were applied to wheat (0, 40, 80, and 120 kg ha−1) and three to maize (0, 60, and 120 kg ha−1). Nitrogen rates to the subsequent wheat crop were superimposed to each one of the preceding maize crop. Crop residues of both crops were left on the soil surface. Results showed that the amount of soil N measured as N–NO3 and N–NH4 was reduced during the first three seasons after the establishment of permanent beds. Although this reduction was substantial, stepwise regression procedures indicated that wheat grain yield was mostly determined by the amount of rainfall and distribution during the crop season, except for the 2009 season when the standard deviation of this measurement was larger. In addition to those N measurements, total soil N, available P, and exchangeable K had no effect on grain yield. The response of grain yield to N application rates >40 kg ha−1 was negligible for both crop rotations. In average, wheat grain yield in rotation was greater than wheat in monoculture. Grain yield reduction in monoculture resulted from fewer heads m−2.