The role of mutations in intraspecific differentiation of groundnut (Arachis hypogaea L.)

Summary Based on morphological diversity, cultivated groundnut (Arachis hypogaea L.) is classified into two subspecies (fastigiata and hypogaea) and further into four botanical types (Spanish bunch, Valencia, Virginia bunch and Virginia runner). In a cross between two Spanish cultivars belonging to ssp. fastigiata, a true breeding variant (Dharwad early runner) sharing some characters of both the subspecies was isolated. The variant, on mutagenesis with ethyl methane sulphonate (EMS) yielded a very high frequency of mutants resembling all four botanical types. Some of the mutants produced germinal reversions to Dharwad early runner in later generations indicating genetic instability. While most of the revertants bred true, some of the mutants continued to segregate, wherein each botanical group of mutants produced all other botanical types. A detailed analysis of the breeding behaviour of mutants revealed several unusual features (such as homozygous mutations, mutation outbursts, segregation distortions, somatic mutations and multiple character mutations) that could not be explained through conventional mutation theory. In the light of these findings, the role of mutations in evolutionary differentiation of the crop and the probable mode of their origin have been discussed.     

A review of downscaling methods for remote sensing-based irrigation management: part I

High-resolution daily evapotranspiration (ET) maps would greatly improve irrigation management. Numerous ET mapping algorithms have been developed to make use of thermal remote sensing data acquired by satellite sensors. However, adoption of remote sensing-based ET maps for irrigation management has not been feasible due to inadequate spatial and temporal resolution of ET maps. Data from a coarse spatial resolution image in agricultural fields often cause inaccurate ET estimation because of a high level of spatial heterogeneity in land use. Image downscaling methods have been utilized to overcome spatial and temporal scaling issues in numerous remote sensing applications. In the field of hydrology, the image downscaling method has been used to improve spatial resolution of remote sensing-based ET maps for irrigation scheduling purposes and thus improves estimation of crop water requirements. This paper (part I) reviews downscaling methods to improve spatial resolution of land surface characteristics such as land surface temperature or ET. Each downscaling method was assessed and compared with respect to their capability of downscaling spatial resolutions of images. The companion paper (part II) presents review of image fusion methods that are also designed to increase spatial resolutions of images by integrating multi-spectral and panchromatic images.     

Hybrid breeding for fall armyworm resistance: Combining ability and hybrid prediction

Fall armyworm (FAW, Spodoptera frugiperda) emerged as a major lepidopteran pest destroying maize in sub-Saharan Africa. A diallel mating design was used to generate 210 experimental hybrids from 21 lines. Experimental hybrids and four checks were evaluated in two locations. Commercial checks suffered higher foliar and ear damage compared to the top 15 hybrids. Mean squares associated with the genotypic variation were higher than genotype-by-environment interactions for foliar and ear damage traits. Heritabilities were moderate to high. Significant correlations were observed between grain yield (GY) with ear rot (−0.54) and ear damage (−0.45). Positive and significant GCA effects were observed for GY in seven parental lines, which were developed from multiple insect resistance breeding programmes. CKSBL10153 has the highest GCA value for GY and shows significant GCA effects for foliar and ear damage traits. These lines were identified as the ideal combiners for GY and FAW resistance and are therefore recommended for utilization as testers in the development of FAW-resistant three-way cross-hybrid maize with correlated response for increased GY. GCA and marker-based prediction correlations of GY were 0.79 and 0.96, respectively. Both GCA effects and marker-based models were effective in predicting hybrid performance for FAW resistance.