Projecting the Contribution of Provitamin A Maize Biofortification and Other Nutrition Interventions to the Nutritional Adequacy and Cost of Diets in Rural Zimbabwe.

BACKGROUND: Evidence of the effectiveness of biofortified maize with higher provitamin A (PVA) to address vitamin A deficiency in rural Africa remains scant. OBJECTIVES: This study projects the impact of adopting PVA maize for a diversity of households in an area typical of rural Zimbabwe and models the cost and composition of diets adequate in vitamin A. METHODS: Household-level weighed food records were generated from 30 rural households during a week in April and November 2021. Weekly household intakes were calculated, as well as indicative costs of diets using data from market surveys. The impact of PVA maize adoption was modeled assuming all maize products contained observed vitamin A concentrations. The composition and cost of the least expensive indicative diets adequate in vitamin A were calculated using linear programming. RESULTS: Very few households would reach adequate intake of vitamin A with the consumption of PVA maize. However, from a current situation of 33%, 50%-70% of households were projected to reach ≥50% of their requirements (the target of PVA), even with the modest vitamin A concentrations achieved on-farm (mean of 28.3 µg RAE per 100 g). This proportion would increase if higher concentrations recorded on-station were achieved. The estimated daily costs of current diets (mean ± standard deviation) were USD 1.43 ± 0.59 in the wet season and USD 0.96 ± 0.40 in the dry season. By comparison, optimization models suggest that diets adequate in vitamin A could be achieved at daily costs of USD 0.97 and USD 0.79 in the wet and dry seasons, respectively. CONCLUSIONS: The adoption of PVA maize would bring a substantial improvement in vitamin A intake in rural Zimbabwe but should be combined with other interventions (e.g., diet diversification) to fully address vitamin A deficiency.

Macronutrient application rescues performance of tolerant sorghum genotypes when infected by the parasitic plant striga.

BACKGROUND AND AIMS: Infection by the hemi-parasitic plant Striga hermonthica causes severe host plant damage and seed production losses. Increased availability of essential plant nutrients reduces infection. Whether, how and to what extent it also reduces striga-induced host plant damage has not been well studied. METHODS: The effects of improved macro- and micronutrient supply on host plant performance under striga-free and infected conditions were investigated in glasshouse pot assays. One striga-sensitive and two striga-tolerant genotypes were compared. Plants growing in impoverished soils were supplied with (1) 25 % of optimal macro- and micronutrient quantities, (2) 25 % macro- and 100 % micronutrients, (3) 100 % macro- and 25 % micronutrients, or (4) 100 % macro- and micronutrients. KEY RESULTS: Photosynthesis rates of striga-infected plants of the sensitive genotype increased with improved nutrition (from 12.2 to 22.1 µmol m-2 s-1) but remained below striga-free levels (34.9-38.8 µmol m-2 s-1). For the tolerant genotypes, increased macronutrient supply offset striga-induced photosynthesis losses. Striga-induced relative grain losses of 100 % for the sensitive genotype were reduced to 74 % by increased macronutrients. Grain losses of 80 % in the tolerant Ochuti genotype, incurred at low nutrient supply, were reduced to 5 % by improved nutrient supply. CONCLUSIONS: Increasing macronutrient supply reduces the impact of striga on host plants but can only restore losses when applied to genotypes with a tolerant background.

Controls on timescales of soil organic carbon persistence across sub-Saharan Africa.

Given the importance of soil for the global carbon cycle, it is essential to understand not only how much carbon soil stores but also how long this carbon persists. Previous studies have shown that the amount and age of soil carbon are strongly affected by the interaction of climate, vegetation, and mineralogy. However, these findings are primarily based on studies from temperate regions and from fine-scale studies, leaving large knowledge gaps for soils from understudied regions such as sub-Saharan Africa. In addition, there is a lack of data to validate modeled soil C dynamics at broad scales. Here, we present insights into organic carbon cycling, based on a new broad-scale radiocarbon and mineral dataset for sub-Saharan Africa. We found that in moderately weathered soils in seasonal climate zones with poorly crystalline and reactive clay minerals, organic carbon persists longer on average (topsoil: 201 ± 130 years; subsoil: 645 ± 385 years) than in highly weathered soils in humid regions (topsoil: 140 ± 46 years; subsoil: 454 ± 247 years) with less reactive minerals. Soils in arid climate zones (topsoil: 396 ± 339 years; subsoil: 963 ± 669 years) store organic carbon for periods more similar to those in seasonal climate zones, likely reflecting climatic constraints on weathering, carbon inputs and microbial decomposition. These insights into the timescales of organic carbon persistence in soils of sub-Saharan Africa suggest that a process-oriented grouping of soils based on pedo-climatic conditions may be useful to improve predictions of soil responses to climate change at broader scales.

Changes in organic carbon to clay ratios in different soils and land uses in England and Wales over time.

Realistic targets for soil organic carbon (SOC) concentrations are needed, accounting for differences between soils and land uses. We assess the use of SOC/clay ratio for this purpose by comparing changes over time in (a) the National Soil Inventory of England and Wales, first sampled in 1978-1983 and resampled in 1994-2003, and (b) two long-term experiments under ley-arable rotations on contrasting soils in the East of England. The results showed that normalising for clay concentration provides a more meaningful separation between land uses than changes in SOC alone. Almost half of arable soils in the NSI had degraded SOC/clay ratios (< 1/13), compared with just 5% of permanent grass and woodland soils. Soils with initially large SOC/clay ratios (≥ 1/8) were prone to greater losses of SOC between the two NSI samplings than those with smaller ratios. The results suggest realistic long-term targets for SOC/clay in arable, ley grass, permanent grass and woodland soils are 1/13, 1/10, and > 1/8, respectively. Given the wide range of soils and land uses across England and Wales in the datasets used to test these targets, they should apply across similar temperate regions globally, and at national to sub-regional scales.

How have smallholder farmers used digital extension tools? Developer and user voices from Sub-Saharan Africa, South Asia and Southeast Asia.

Digital extension tools (DETs) include phone calls, WhatsApp groups and specialised smartphone applications used for agricultural knowledge brokering. We researched processes through which DETs have (and have not) been used by farmers and other extension actors in low- and middle-income countries. We interviewed 40 DET developers across 21 countries and 101 DET users in Bihar, India. We found DET use is commonly constrained by fifteen pitfalls (unawareness of DET, inaccessible device, inaccessible electricity, inaccessible mobile network, insensitive to digital illiteracy, insensitive to illiteracy, unfamiliar language, slow to access, hard to interpret, unengaging, insensitive to user's knowledge, insensitive to priorities, insensitive to socio-economic constraints, irrelevant to farm, distrust). These pitfalls partially explain why women, less educated and less wealthy farmers often use DETs less, as well as why user-driven DETs (e.g. phone calls and chat apps) are often used more than externally-driven DETs (e.g. specialised smartphone apps). Our second key finding was that users often made - not just found - DETs useful for themselves and others. This suggests the word 'appropriation' conceptualises DET use more accurately and helpfully than the word 'adoption'. Our final key finding was that developers and users advocated almost ubiquitously for involving desired users in DET provision. We synthesise these findings in a one-page framework to help funders and developers facilitate more useable, useful and positively impactful DETs. Overall, we conclude developers increase DET use by recognizing users as fellow developers - either through collaborative design or by designing adaptable DETs that create room for user innovation.

Portable X-ray fluorescence (pXRF) calibration for analysis of nutrient concentrations and trace element contaminants in fertilisers.

With the increasing popularity of local blending of fertilisers, the fertiliser industry faces issues regarding quality control and fertiliser adulteration. Another problem is the contamination of fertilisers with trace elements that have been shown to subsequently accumulate in the soil and be taken up by plants, posing a danger to the environment and human health. Conventional characterisation methods necessary to ensure the quality of fertilisers and to comply with local regulations are costly, time consuming and sometimes not even accessible. Alternatively, using a wide range of unamended and intentionally amended fertilisers this study developed empirical calibrations for a portable handheld X-ray fluorescence (pXRF) spectrometer, determined the reliability for estimating the macro and micro nutrients and evaluated the use of the pXRF for the high-throughput detection of trace element contaminants in fertilisers. The models developed using pXRF for Mg, P, S, K, Ca, Mn, Fe, Zn and Mo had R2 values greater or equal to 0.97. These models also performed well on validation, with R2 values greater or equal to 0.97 (except for Fe, R2val = 0.55) and slope values ranging from 0.81 to 1.44. A second set of models were developed with a focus on trace elements in amended fertilisers. The R2 values of calibration for Co, Ni, As, Se, Cd and Pb were greater than or equal to 0.80. At concentrations up to 1000 mg kg-1, good validation statistics were also obtained; R2 values ranged from 0.97-0.99, except in one instance. The regression coefficients of the validation also had good prediction in the range of 0-100 mg kg-1 (R2 values were from 0.78-0.99), but not as well at lower concentrations up to 20 mg kg-1 (R2 values ranged from 0.10-0.99), especially for Cd. This study has demonstrated that pXRF can measure several major (P, Ca) and micro (Mn, Fe, Cu) nutrients, as well as trace elements and potential contaminants (Cr, Ni, As) in fertilisers with high accuracy and precision. The results obtained in this study is good, especially considering that loose powders were scanned for a maximum of 90 seconds without the use of a vacuum pump.

Optimizing setup of scan number in FTIR spectroscopy using the moment distance index and PLS regression: application to soil spectroscopy.

Vibrational spectroscopy such as Fourier-transform infrared (FTIR), has been used successfully for soil diagnosis owing to its low cost, minimal sample preparation, non-destructive nature, and reliable results. This study aimed at optimizing one of the essential settings during the acquisition of FTIR spectra (viz. Scans number) using the standardized moment distance index (SMDI) as a metric that could trap the fine points of the curve and extract optimal spectral fingerprints of the sample. Furthermore, it can be used successfully to assess the spectra resemblance. The study revealed that beyond 50 scans the similarity of the acquisitions has been remarkably improved. Subsequently, the effect of the number of scans on the predictive ability of partial least squares regression models for the estimation of five selected soil properties (i.e., soil pH in water, soil organic carbon, total nitrogen, cation exchange capacity and Olsen phosphorus) was assessed, and the results showed a general tendency in improving the correlation coefficient (R2) as the number of scans increased from 10 to 80. In contrast, the cross-validation error RMSECV decreased with increasing scan number, reflecting an improvement of the predictive quality of the calibration models with an increasing number of scans.

African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning.

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples ([Formula: see text]) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable-phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)-silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images-SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature-however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.

Plant Available Zinc Is Influenced by Landscape Position in the Amhara Region, Ethiopia.

Zinc (Zn) is an important element determining the grain quality of staple food crops and deficient in many Ethiopian soils. However, farming systems are highly variable in Ethiopia due to different soil types and landscape cropping positions. Zinc availability and uptake by plants from soil and fertilizer sources are governed by the retention and release potential of the soil, usually termed as adsorption and desorption, respectively. The aim of this study was to characterize the amount of plant available Zn at different landscape positions. During the 2018/19 cropping season, adsorption-desorption studies were carried out on soil samples collected from on-farm trials conducted at Aba Gerima, Debre Mewi and Markuma in the Amhara Region. In all locations and landscape positions, adsorption and desorption increased with increasing Zn additions. The amount of adsorption and desorption was highly associated with the soil pH, the soil organic carbon concentration and cation exchange capacity, and these factors are linked to landscape positions. The Freundlich isotherm fitted very well to Zn adsorption (r2 0.87-0.99) and desorption (r2 0.92-0.99), while the Langmuir isotherm only fitted to Zn desorption (r2 0.70-0.93). Multiple regression models developed by determining the most influential soil parameters for Zn availability could be used to inform Zn fertilizer management strategies for different locations and landscape positions in this region, and thereby improve plant Zn use efficiency.

Combining host plant defence with targeted nutrition: key to durable control of hemiparasitic Striga in cereals in sub-Saharan Africa?

Host plant defence mechanisms (resistance and tolerance) and plant nutrition are two of the most widely proposed components for the control of hemiparasitic weeds of the genus Striga in tropical cereal production systems. Neither of the two components alone is effective enough to prevent parasitism and concomitant crop losses. This review explores the potential of improved plant nutrition, being the chemical constituent of soil fertility, to fortify the expression of plant inherent resistance and tolerance against Striga. Beyond reviewing advances in parasitic plant research, we assess relevant insights from phytopathology and plant physiology in the broader sense to identify opportunities and knowledge gaps and to develop the way forward regarding research and development of combining genetics and plant nutrition for the durable control of Striga.

A comparison of soil texture measurements using mid-infrared spectroscopy (MIRS) and laser diffraction analysis (LDA) in diverse soils.

Spectroscopic methods for the determination of soil texture are faster and cheaper than the standard methods, but how do the results compare? To address this question, laser diffraction analysis (LDA) and mid-infrared spectroscopy (MIRS) analysis have been compared to conventional sieve-pipette measurements of texture in diverse European and Kenyan soils. To our knowledge this comparison between LDA and MIRS has not been made previously. It has used soils with a broad range of organic carbon (OC) contents to investigate whether, as in other techniques, clay-OC aggregation affects the estimation of clay with MIRS. The MIRS predictions of clay content were much better than the LDA measurements, but both techniques gave good measurements of sand content. The MIRS over-estimated clay at low clay content and under-estimated at high clay content (calibration set R2 = 0.83). The LDA over-estimated clay by ~ 60% (calibration set R2 = 0.36), indicating that the widely used clay threshold of < 8 µm was too high, and < 4 µm was found to be more accurate. In samples with < 5% OC content, both the LDA and MIRS gave very good clay predictions (R2 = 0.88 and 0.81, respectively). But in predictions of clay content in samples with > 5% OC the LDA under-estimated (R2 = < 0.1) and MIRS over-estimated (R2 = 0.34) clay content. In soils with OC removed, the MIRS prediction of clay content improved, indicating interference between over-lapping spectral regions for organic and mineral constituents. Unlike granulometric measurements of texture such as the LDA, MIRS analysis is not subject to the limitations imposed by the shape and density of particles. It was concluded that in typical agricultural soils with < 5% OC and < 60% clay content, both techniques could be used for cheap, fast and reliable estimates of soil texture.

Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning.

Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. Here, we provide an assessment of these methods for the analysis of total Carbon, Nitrogen and total elemental composition of multiple elements in organic amendments. We developed machine learning methods to rapidly quantify the concentrations of macro- and micronutrient elements present in the samples and propose a novel system for the quality assessment of organic amendments. Two types of machine learning methods, forest regression and extreme gradient boosting, were used with data from both pXRF and DRIFT-MIR spectroscopy. Cross-validation trials were run to evaluate generalizability of models produced on each instrument. Both methods demonstrated similar broad capabilities in estimating nutrients using machine learning, with pXRF being suitable for nutrients and contaminants. The results make portable spectrometry in combination with machine learning a scalable solution to provide comprehensive nutrient analysis for organic amendments.

DREB/CBF expression in wheat and barley using the stress-inducible promoters of HD-Zip I genes: impact on plant development, stress tolerance and yield.

Networks of transcription factors regulate diverse physiological processes in plants to ensure that plants respond to abiotic stresses rapidly and efficiently. In this study, expression of two DREB/CBF genes, TaDREB3 and TaCBF5L, was modulated in transgenic wheat and barley, by using stress-responsive promoters HDZI-3 and HDZI-4. The promoters were derived from the durum wheat genes encoding the γ-clade TFs of the HD-Zip class I subfamily. The activities of tested promoters were induced by drought and cold in leaves of both transgenic species. Differences in sensitivity of promoters to drought strength were dependent on drought tolerance levels of cultivars used for generation of transgenic lines. Expression of the DREB/CBF genes under both promoters improved drought and frost tolerance of transgenic barley, and frost tolerance of transgenic wheat seedlings. Expression levels of the putative TaCBF5L downstream genes in leaves of transgenic wheat seedlings were up-regulated under severe drought, and up- or down-regulated under frost, compared to those of control seedlings. The application of TaCBF5L driven by the HDZI-4 promoter led to the significant increase of the grain yield of transgenic wheat, compared to that of the control wild-type plants, when severe drought was applied during flowering; although no yield improvements were observed when plants grew under well-watered conditions or moderate drought. Our findings suggest that the studied HDZI promoters combined with the DREB/CBF factors could be used in transgenic cereal plants for improvement of abiotic stress tolerance, and the reduction of negative influence of transgenes on plant development and grain yields.

Genome-wide association mapping of grain yield in a diverse collection of spring wheat (Triticum aestivum L.) evaluated in southern Australia.

Wheat landraces, wild relatives and other 'exotic' accessions are important sources of new favorable alleles. The use of those exotic alleles is facilitated by having access to information on the association of specific genomic regions with desirable traits. Here, we conducted a genome-wide association study (GWAS) using a wheat panel that includes landraces, synthetic hexaploids and other exotic wheat accessions to identify loci that contribute to increases in grain yield in southern Australia. The 568 accessions were grown in the field during the 2014 and 2015 seasons and measured for plant height, maturity, spike length, spike number, grain yield, plant biomass, HI and TGW. We used the 90K SNP array and two GWAS approaches (GAPIT and QTCAT) to identify loci associated with the different traits. We identified 17 loci with GAPIT and 25 with QTCAT. Ten of these loci were associated with known genes that are routinely employed in marker assisted selection such as Ppd-D1 for maturity and Rht-D1 for plant height and seven of those were detected with both methods. We identified one locus for yield per se in 2014 on chromosome 6B with QTCAT and three in 2015, on chromosomes 4B and 5A with GAPIT and 6B with QTCAT. The 6B loci corresponded to the same region in both years. The favorable haplotypes for yield at the 5A and 6B loci are widespread in Australian accessions with 112 out of 153 carrying the favorable haplotype at the 5A locus and 136 out of 146 carrying the favorable haplotype at the 6A locus, while the favorable haplotype at 4B is only present in 65 out of 149 Australian accessions. The low number of yield QTL in our study corroborate with other GWAS for yield in wheat, where most of the identified loci have very small effects.

Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging.

BACKGROUND: Unmanned aerial vehicles offer the opportunity for precision agriculture to efficiently monitor agricultural land. A vegetation index (VI) derived from an aerially observed multispectral image (MSI) can quantify crop health, moisture and nutrient content. However, due to the high cost of multispectral sensors, alternate, low-cost solutions have lately received great interest. We present a novel method for model-based estimation of a VI using RGB color images. The non-linear spatio-spectral relationship between the RGB image of vegetation and the index computed by its corresponding MSI is learned through deep neural networks. The learned models can be used to estimate VI of a crop segment. RESULTS: Analysis of images obtained in wheat breeding trials show that the aerially observed VI was highly correlated with ground-measured VI. In addition, VI estimates based on RGB images were highly correlated with VI deduced from MSIs. Spatial, spectral and temporal information of images contributed to estimation of VI. Both intra-variety and inter-variety differences were preserved by estimated VI. However, VI estimates were reliable until just before significant appearance of senescence. CONCLUSION: The proposed approach validates that it is reasonable to accurately estimate VI using deep neural networks. The results prove that RGB images contain sufficient information for VI estimation. It demonstrates that low-cost VI measurement is possible with standard RGB cameras.

Overexpression of the class I homeodomain transcription factor TaHDZipI-5 increases drought and frost tolerance in transgenic wheat.

Characterization of the function of stress-related genes helps to understand the mechanisms of plant responses to environmental conditions. The findings of this work defined the role of the wheat TaHDZipI-5 gene, encoding a stress-responsive homeodomain-leucine zipper class I (HD-Zip I) transcription factor, during the development of plant tolerance to frost and drought. Strong induction of TaHDZipI-5 expression by low temperatures, and the elevated TaHDZipI-5 levels of expression in flowers and early developing grains in the absence of stress, suggests that TaHDZipI-5 is involved in the regulation of frost tolerance at flowering. The TaHDZipI-5 protein behaved as an activator in a yeast transactivation assay, and the TaHDZipI-5 activation domain was localized to its C-terminus. The TaHDZipI-5 protein homo- and hetero-dimerizes with related TaHDZipI-3, and differences between DNA interactions in both dimers were specified at 3D molecular levels. The constitutive overexpression of TaHDZipI-5 in bread wheat significantly enhanced frost and drought tolerance of transgenic wheat lines with the appearance of undesired phenotypic features, which included a reduced plant size and biomass, delayed flowering and a grain yield decrease. An attempt to improve the phenotype of transgenic wheat by the application of stress-inducible promoters with contrasting properties did not lead to the elimination of undesired phenotype, apparently due to strict spatial requirements for TaHDZipI-5 overexpression.

Phenotyping of plants in competitive but controlled environments: a study of drought response in transgenic wheat.

In recent years, the interest in new technologies for wheat improvement has increased greatly. To screen genetically modified germplasm in conditions more realistic for a field situation we developed a phenotyping platform where transgenic wheat and barley are grown in competition. In this study, we used the platform to (1) test selected promoter and gene combinations for their capacity to increase drought tolerance, (2) test the function and power of our platform to screen the performance of transgenic plants growing in competition, and (3) develop and test an imaging and analysis process as a means of obtaining additional, non-destructive data on plant growth throughout the whole growth cycle instead of relying solely on destructive sampling at the end of the season. The results showed that several transgenic lines under well watered conditions had higher biomass and/or grain weight than the wild-type control but the advantage was significant in one case only. None of the transgenics seemed to show any grain weight advantage under drought stress and only two lines had a substantially but not significantly higher biomass weight than the wild type. However, their evaluation under drought stress was disadvantaged by their delayed flowering date, which increased the drought stress they experienced in comparison to the wild type. Continuous imaging during the season provided additional and non-destructive phenotyping information on the canopy development of mini-plots in our phenotyping platform. A correlation analysis of daily canopy coverage data with harvest metrics showed that the best predictive value from canopy coverage data for harvest metrics was achieved with observations from around heading/flowering to early ripening whereas early season observations had only a limited diagnostic value. The result that the biomass/leaf development in the early growth phase has little correlation with biomass or grain yield data questions imaging approaches concentrating only on the early development stage.

Unmasking Novel Loci for Internal Phosphorus Utilization Efficiency in Rice Germplasm through Genome-Wide Association Analysis.

Depletion of non-renewable rock phosphate reserves and phosphorus (P) fertilizer price increases has renewed interest in breeding P-efficient varieties. Internal P utilization efficiency (PUE) is of prime interest because there has been no progress to date in breeding for high PUE. We characterized the genotypic variation for PUE present within the rice gene pool by using a hydroponic system that assured equal plant P uptake, followed by mapping of loci controlling PUE via Genome-Wide Association Studies (GWAS). Loci associated with PUE were mapped on chromosomes 1, 4, 11 and 12. The highest PUE was associated with a minor indica-specific haplotype on chromosome 1 and a rare aus-specific haplotype on chromosome 11. Comparative variant and expression analysis for genes contained within the chromosome 1 haplotype identified high priority candidate genes. Differences in coding regions and expression patterns between genotypes of contrasting haplotypes, suggested functional alterations for two predicted nucleic acid-interacting proteins that are likely causative for the observed differences in PUE. The loci reported here are the first identified for PUE in any crop that is not confounded by differential P uptake among genotypes. Importantly, modern rice varieties lacked haplotypes associated with superior PUE, and would thus benefit from targeted introgressions of these loci from traditional donors to improve plant growth in phosphorus-limited cropping systems.

Drought yield index to select high yielding rice lines under different drought stress severities.

BACKGROUND: Drought is the most severe abiotic stress reducing rice yield in rainfed drought prone ecosystems. Variation in intensity and severity of drought from season to season and place to place requires cultivation of rice varieties with different level of drought tolerance in different areas. Multi environment evaluation of breeding lines helps breeder to identify appropriate genotypes for areas prone to similar level of drought stress. From a set of 129 advanced rice (Oryza sativa L.) breeding lines evaluated under rainfed drought-prone situations at three locations in eastern India from 2005 to 2007, a subset of 39 genotypes that were tested for two or more years was selected to develop a drought yield index (DYI) and mean yield index (MYI) based on yield under irrigated, moderate and severe reproductive-stage drought stress to help breeders select appropriate genotypes for different environments. RESULTS: ARB 8 and IR55419-04 recorded the highest drought yield index (DYI) and are identified as the best drought-tolerant lines. The proposed DYI provides a more effective assessment as it is calculated after accounting for a significant genotype x stress-level interaction across environments. For rainfed areas with variable frequency of drought occurrence, Mean yield index (MYI) along with deviation in performance of genotypes from currently cultivated popular varieties in all situations helps to select genotypes with a superior performance across irrigated, moderate and severe reproductive-stage drought situations. IR74371-70-1-1 and DGI 75 are the two genotypes identified to have shown a superior performance over IR64 and MTU1010 under all situations. CONCLUSION: For highly drought-prone areas, a combination of DYI with deviation in performance of genotypes under irrigated situations can enable breeders to select genotypes with no reduction in yield under favorable environments compared with currently cultivated varieties. For rainfed areas with variable frequency of drought stress, use of MYI together with deviation in performance of genotypes under different situations as compared to presently cultivated varieties will help breeders to select genotypes with superior performance under all situations.