Prediction Of Grain Yield Research Articles

Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer – a case study of small farmlands in the South of China

Timely and accurate crop monitoring and yield forecasting before harvesting are valuable for precision management, policy and decision making, and marketing. The aim of this study is to explore the potential of fusing spectral and structural information extracted from the unmanned aerial vehicle (UAV)-based images in the whole growth period of rice to improve the grain yield prediction. A UAV platform carrying RGB and multispectral cameras was employed to collect high spatial resolution images of the rice crop under different nitrogen treatments over two years. The vegetation indices (VIs), canopy height and canopy coverage were extracted from UAV-based images, which were then used to develop random forest prediction models for grain yield. Among all of the investigated VIs, it was found that normalized difference yellowness index (NDYI) was the most useful index to monitor the changes in leaf chlorophyll content as well as the leaf greenness during the whole growth period. Meanwhile, the VIs provided a comparable prediction of grain yield to field-measured aboveground biomass and leaf chlorophyll content. Fusion of the multi-temporal normalized difference vegetation index (NDVI), NDYI, canopy height and canopy coverage achieved the best prediction of grain yield with a determination coefficient of 0.85 and 0.83, and relative root mean square error of 3.56% and 2.75% in 2017 and 2018, respectively, which outperformed the results in the reported studies. The initial heading stage was the optimal growth stage for the prediction of grain yield. Furthermore, the robustness of prediction model developed from the dataset in 2017 was validated by an external dataset from 2018 using model transfer. These findings demonstrate that the proposed approach can improve the prediction accuracy of grain yield as well as achieve an efficient monitoring of crop growth.

A Prediction of Grain Yield Based on Hybrid Intelligent Algorithm

The prediction is most important goals in economic quantitative studies, it basis in design and plan future economic policies properly process over forecasting accuracy. This paper is aiming at the problem salp swarm algorithm (SSA) for predicting grain yield is prone to fall into the local optimal problem. An improved SSA is proposed with combine with back propagation neural network. Using the different advantages of SSA algorithm in global search capabilities, combining the two for further optimize the weight, improve the accuracy and robustness of the grain yield prediction model. The specific implementation is selected from 1963 to 2013.These methods are used to define agricultural datasets that supports crop growth decision for grain product and its influencing factors were tested as a data set.The results show that, the improved salp swarm optimization can be classified as a good predict tool for the domestic food production trend in recent years compared with the SSA. This paper briefly introduces three artificial methods BP neural networks, SSA and improved SSA optimization algorithm. The natural behavior of salp, barrel-shaped plankton that are mostly water by weight optimization and combined with mixed-group of intelligent algorithm are simulated. The simulation results of grain production prediction illustrate that the predict precision of the improved SSA is much higher than of both conventional BPNN and SSA techniques and it’s very efficient and practicable.

Estimating aboveground and organ biomass of plant canopies across the entire season of rice growth with terrestrial laser scanning

Non-destructive and accurate estimation of crop biomass is crucial for the quantitative diagnosis of growth status and timely prediction of grain yield. As an active remote sensing technique, terrestrial laser scanning (TLS) has become increasingly available in crop monitoring for its advantages in recording structural properties. Some researchers have attempted to use TLS data in the estimation of crop aboveground biomass, but only for part of the growing season. Previous studies rarely investigated the estimation of biomass for individual organs, such as the panicles in rice canopies, which led to the poor understanding of TLS technology in monitoring biomass partitioning among organs. The objective of this study was to investigate the potential of TLS in estimating the biomass for individual organs and aboveground biomass of rice and to examine the feasibility of developing universal models for the entire growing season. The field plots experiments were conducted in 2017 and 2018 and involved different nitrogen (N) rates, planting techniques and rice varieties. Three regression approaches, stepwise multiple linear regression (SMLR), random forest regression (RF) and linear mixed-effects (LME) modeling, were evaluated in estimating biomass with extensive TLS and biomass data collected at multiple phenological stages of rice growth across the entire season. The models were calibrated with the 2017 dataset and validated independently with the 2018 dataset.The results demonstrated that growth stage in LME modeling was selected as the most significant random effect on rice growth among the three candidates, which were rice variety, growth stage and planting technique. The LME models grouped by growth stage exhibited higher validation accuracies for all biomass variables over the entire season to varying degrees than SMLR models and RF models. The most pronounced improvement with a LME model was obtained for panicle biomass, with an increase of 0.74 in R2 (LME: R2 = 0.90, SMLR: R2 = 0.16) and a decrease of 1.15 t/ha in RMSE (LME: RMSE =0.79 t/ha, SMLR: RMSE =2.94 t/ha). Compared to SMLR and RF, LME modeling yielded similar estimation accuracies of aboveground biomass for pre-heading stages, but significantly higher accuracies for post-heading stages (LME: R2 = 0.63, RMSE =2.27 t/ha; SMLR: R2 = 0.42, RMSE =2.42 t/ha; RF: R2 = 0.57, RMSE =2.80 t/ha). These findings implied that SMLR was only suitable for the estimation of biomass at pre-heading stages and LME modeling performed remarkably well across all growth stages, especially for post-heading. The results suggest coupling TLS with LME modeling is a promising approach to monitoring rice biomass at post-heading stages at high accuracy and to overcoming the saturation of canopy reflectance signals encountered in optical remote sensing. It also has great potential in the monitoring of other crops in cloud-cover conditions and the instantaneous prediction of grain yield any time before harvest.

Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation.

Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.)

Abstract The aim of this study was to compare the Multiple Linear Regression and Artificial Neural Network models in prediction of grain yield of ten landrace varieties of lima bean and evaluate adaptability and stability through the Lin and Binns method for identification of the best performing variety. Trials were conducted in the municipalities of Teresina, PI, and Sao Domingos do Maranhao, MA, through measurement of 12 traits, except for grain yield in Sao Domingos do Maranhao. The parameters of Pearson and Spearman correlation, root mean square error, mean absolute error, and coefficient of determination were used to compare the models. The Artificial Neural Network proved to be more adequate for prediction of grain yield. Adaptability and stability analyses indicated that the environments are discriminant for selection of promising genotypes, and that the landrace variety Mulatinha can be recommended for planting in the municipalities.

Improving sorghum productivity under changing climatic conditions: A modelling approach

Climate variability and change will have far reaching consequences for smallholder farmers in sub-Saharan Africa, the majority of whom depend on agriculture for their livelihoods. Crop modelling can help inform the improvement of agricultural productivity under future climate. This study applies the Agricultural Production Systems sIMulator (APSIM) to assessing the impacts of projected climate change on two (early and medium maturing) sorghum varieties under different management practices. Results show high model accuracy with excellent agreement between simulated and observed values for crop phenology and leaf number per plant. The prediction of grain yield and total biomass of an early maturing variety was fair RMSEn (22.9 and 23.1%), while that of the medium maturing was highly accurate RMSEn (14.9 and 11.9%). Sensitivity analysis performed by changing the calibrated variables of key plant traits in the model, showed higher significant yield change by + or - 10 % changed in radiation use efficiency, (RUE), coefficient extinction (Coeff_ext) and Phyllocron (Phyllo) for early maturing variety while + or - 10 % changed in phyllochron and RUE showed a significant yield change for the medium maturing variety. Under climate change scenerios using RCP 8.5, the simulated yield for the early–maturing variety revealed high inter-annual variability and potential yield loss of 3.3% at Bamako and 1% at Kano in the near-future (2010–2039) compared to baseline (1980–2009). The mid-century (2040–2069) projected yield decline by 4.8% at Bamako and 6.2% at Kano compared to baseline (1980–2009). On the contrary, the medium maturing variety indicated significantly yield gain with high yielding potential in both climate regimes compared to the baseline period (1980–2009). The simulated grain yield increased by 7.2% at Bamako and 4.6% at Kano, in the near-future (2010–2039) while in the mid-century (2040–2069) projected yield increase of 12.3% and 2% at Bamako and Kano compared to baseline (1980–2009). Adaptation strategies under climate change for varying sowing dates in the near-future (2010–2039, indicated that June sowing had a higher positive yield gained over July and August sowing for early maturing variety; July sowing simulated positive gained by 5 -11% over June and August sowing for medium maturing variety in both locations. Similarly, under the mid-century (2040–2069), among the sowing dates and in both locations, June sowing indicates lowest negative yield change over July and August sowing for early maturing variety. However, for medium maturing variety, July sowing had the highest yield gain of 16% over June and August sowing at Bamako and June highest positive yield gained of 11.4% over July and August at Kano. Our study has, therefore, demonstrated the capacity of APSIM model as a tool for testing management, plant traits practices and adoption of improved variety for enhancing the adaptive capacity of smallholder farmers under climate change in the Sudanian zone of West Africa. This approach offers a promising option to design more resilient and productive farming systems for West Africa using the diverse sorghum germplasm available in the region.

Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods.

Although durum wheat (Triticum turgidum var. durum Desf.) is a minor cereal crop representing just 5–7% of the world’s total wheat crop, it is a staple food in Mediterranean countries, where it is used to produce pasta, couscous, bulgur and bread. In this paper, we cover multi-trait prediction of grain yield (GY), days to heading (DH) and plant height (PH) of 270 durum wheat lines that were evaluated in 43 environments (country–location–year combinations) across a broad range of water regimes in the Mediterranean Basin and other locations. Multi-trait prediction analyses were performed by implementing a multi-trait deep learning model (MTDL) with a feed-forward network topology and a rectified linear unit activation function with a grid search approach for the selection of hyper-parameters. The results of the multi-trait deep learning method were also compared with univariate predictions of the genomic best linear unbiased predictor (GBLUP) method and the univariate counterpart of the multi-trait deep learning method (UDL). All models were implemented with and without the genotype × environment interaction term. We found that the best predictions were observed without the genotype × environment interaction term in the UDL and MTDL methods. However, under the GBLUP method, the best predictions were observed when the genotype × environment interaction term was taken into account. We also found that in general the best predictions were observed under the GBLUP model; however, the predictions of the MTDL were very similar to those of the GBLUP model. This result provides more evidence that the GBLUP model is a powerful approach for genomic prediction, but also that the deep learning method is a practical approach for predicting univariate and multivariate traits in the context of genomic selection.

Genomic prediction of grain yield in contrasting environments for white lupin genetic resources

Germplasm collections hold several thousands of white lupin (Lupinus albus L.) accessions. Genome-enabled models with good predictive ability for specific environments could provide a cost-efficient means to identify promising genetic resources for breeding programmes. This study provided an unprecedented assessment of genome-enabled predictions for white lupin grain yield, focusing on (i) a world collection of 109 landraces and 8 varieties phenotyped in three European sites with contrasting climate (Mediterranean, subcontinental or oceanic) and sowing time (data set 1); (ii) 78 geographically diversified landrace genotypes and three variety genotypes phenotyped in moisture-favourable and severely drought-prone managed environments (data set 2). The interest of predictions for individual genotypes was justified by large within-landrace variation for yield responses. Ridge regression BLUP (rrBLUP) and Bayesian Lasso (BL) models exploited allele frequencies (estimated from 3 to 4 genotypes per landrace) of 10,782 polymorphic SNPs for data set 1, and allele values of 9937 polymorphic SNPs for data set 2, following ApeKI-based genotyping-by-sequencing characterization. Compared with BL, rrBLUP displayed similar predictive ability for data set 1 and better predictive ability for data set 2. Best-predictive models displayed intra-environment predictive ability for the five test environments in the range 0.47–0.76. Cross-environment predictions between pairs of environments with positive genetic correlation, i.e., autumn-sown subcontinental vs Mediterranean sites, and moisture-favourable vs drought-prone environments, exhibited a predictive ability range of 0.40–0.51 and a predictive accuracy range of 0.48–0.61. Our results support the exploitation of genomic predictions and provide economic justification for the genotyping of germplasm collections of white lupin.

Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis.

Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.

Scanner-Based Minirhizotrons Help to Highlight Relations between Deep Roots and Yield in Various Wheat Cultivars under Combined Water and Nitrogen Deficit Conditions

Breeding for crops in the context of climate change necessitates phenotyping tools for roots in field conditions. Such in-field phenotyping requires the development of rapid and non-destructive measurement techniques for the screening of relevant root traits under sub-optimal conditions. In this study, we used scanner-based minirhizotrons to measure in situ the root length and surface/volume densities of roots for four wheat varieties, under four different growth conditions: irrigated and rainfed coupled with optimal and sub-optimal N fertilization under a Mediterranean climate. For all the treatments, grain yield correlates with minirhizotron-based root surface density measured at anthesis (r2 = 0.48). Irrigated and rainfed conditions led to contrasted relations between roots and grain yield: no correlation was found in irrigated plots, while under rainfed conditions and sub-optimal fertilization, the higher yields are related to a higher root colonization of the deeper soil layers (r2 = 0.40). Shoot biomass was correlated to grain yield in irrigated conditions, but not in rainfed conditions. However, for the latter, the total root weight, the proportion of which being mainly located in the top soil, is not related to the grain yield. In this way, we show the relationship between these higher grain yields and a stress avoidance mechanism of the root system characterized by a higher root density in the deep soil layers. Thus, unlike shoot biomass measurements, scanner-based minirhizotron allows the direct detection of such a stress-related root development, and therefore opens the door to a better prediction of grain yield.

Uncertainty of CERES-Maize Calibration under Different Irrigation Strategies Using PEST Optimization Algorithm

An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to explore model calibration uncertainties. After calibration for each subset, PEST also generated 300 cultivar parameter sets by assuming a normal distribution of each parameter within their reported values in the literature, using the Latin hypercube sampling (LHS) method. The parameter sets that produced similar goodness of fit (11–164 depending on subset used for calibration) were then used to predict all the treatments and years of the entire dataset. Our results showed that the selection of calibration datasets greatly affected the calibrated crop parameters and their uncertainty, as well as prediction uncertainty of grain yield and biomass. The high variability in model prediction of grain yield and biomass among the six (Calibration-by-Treatment) or the four (Calibration-by-Year) scenarios indicated that parameter uncertainty should be considered in calibrating CERES-Maize with grain yield and biomass data from different irrigation treatments, and model predictions should be provided with confidence intervals.

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat.

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.

Low-cost assessment of grain yield in durum wheat using RGB images

The pattern of photosynthetic area of the canopy throughout the crop cycle is an important factor for determining grain yield in wheat. This work proposes the use of zenithal RGB images of the canopy taken in natural light conditions to derive vegetation indices as a low-cost approach to predict grain yield. A set of 23 varieties of durum wheat was monitored in three growing conditions (support irrigation, rainfed and late planting) and two sites (Aranjuez and Valladolid, Spain), totalling 6 field trials. For each plot, digital RGB images were taken periodically from seedling emergence to late grain filling. RGB-based Green Area (GA), Greener Area (GGA), Normalized Green Red Difference Index (NGRDI), Triangular Greenness Index (TGI) and a novel photosynthetic area index based on the CIE L*u*v* colour space (u*v*A) were compared to handheld spectroradiometer Normalised Difference Vegetation Index (NDVI) for reference. In the case of the irrigated and late planting trials the best phenotypic predictions of grain yield were achieved with the vegetation indices measured during the last part of the crop cycle (i.e. grain filling). For the rainfed trials the best phenotypic predictions were achieved with indices measured earlier (around heading). Among all the evaluated indices, the novel index performed the best. Considering the heritabilities of the evaluated RGB indices and their genetic correlations with grain yield, index-based predictions of grain yield were best in the early crop stages for both rainfed and irrigated conditions, while for late planting indices measured at different crop stages performed equally well.

Vegetation indices in the prediction of biomass and grain yield of white oat under irrigation levels

ABSTRACT Vegetation indices are widely used to indicate the nutritional status of crops, as well as to estimate their harvest yield. However, their accuracy is influenced by the phenological stage of evaluation and the index used. The present study aimed to evaluate the accuracy of the Normalized Difference Vegetation Index (NDVI) and Inverse Ratio Vegetation Index (IRVI) in the prediction of grain yield and biomass of white oat cultivated under irrigation levels, besides indicating the best phenological stage for evaluation. The irrigation levels consisted of 11 %, 31 %, 60 %, 87 % and 100 % of the maximum evapotranspiration, with four replicates. The mean values for NDVI and IRVI were determined using an active terrestrial sensor, at four phenological stages (4, 8, 10 and 10.5.4). The white oat grain yield and biomass may be estimated with a high precision using the NDVI and IRVI. The NDVI was more accurate than the IRVI. The grain yield estimate was more accurate from the flag leaf sheath appearance stage (10), whereas, for the biomass, the best estimate was for the kernel watery ripe stage (10.5.4).

Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images

BackgroundThe number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or as a phenotyping trait in breeding programs. Currently the number of ears is counted manually, which is time consuming. Moreover, there is no single standardized protocol for counting the ears. An automatic ear-counting algorithm is proposed to estimate ear density under field conditions based on zenithal color digital images taken from above the crop in natural light conditions. Field trials were carried out at two sites in Spain during the 2014/2015 crop season on a set of 24 varieties of durum wheat with two growing conditions per site. The algorithm for counting uses three steps: (1) a Laplacian frequency filter chosen to remove low and high frequency elements appearing in an image, (2) a Median filter to reduce high noise still present around the ears and (3) segmentation using Find Maxima to segment local peaks and determine the ear count within the image.ResultsThe results demonstrate high success rate (higher than 90%) between the algorithm counts and the manual (image-based) ear counts, and precision, with a low standard deviation (around 5%). The relationships between algorithm ear counts and grain yield was also significant and greater than the correlation with manual (field-based) ear counts. In this approach, results demonstrate that automatic ear counting performed on data captured around anthesis correlated better with grain yield than with images captured at later stages when the low performance of ear counting at late grain filling stages was associated with the loss of contrast between canopy and ears.ConclusionsDeveloping robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic component of yield, is highly relevant for phenotyping efforts towards increases in grain yield. Although the phenological stage of measurements is important, the robust image analysis algorithm presented here appears to be amenable from aerial or other automated platforms.

Mid-season prediction of grain yield and protein content of spring barley cultivars using high-throughput spectral sensing

Abstract The ability to forecast grain yields and protein contents of spring barley is of particular interest for the malting and brewing industry, as well as for plant breeding. However, methods for early prediction of grain yield and protein content should ideally be timesaving, non-destructive and inexpensive. In this 3-year study using the mobile phenotyping platform PhenoTrac 4, proximally sensed reflectance data of 34 cultivars were used to develop vegetation indices and to calibrate PLSR models, followed by subsequent validation in independent field trials. A comparison among PLSR, the NDVI and REIP indices and an optimized vegetation index indicated that PLSR and REIP (R 2 = 0.71-0.95) gave superior predictions of grain yield. Furthermore, it was possible to distinguish the performance of different cultivars. In contrast, protein content could not be predicted reliably. As an alternative, a PLSR model of leaf N uptake at anthesis was tested to predict grain protein content. Satisfactory correlations were obtained with R 2 = 0.61, but protein content was considerably overestimated. The results show that tractor-based proximal sensing is a high-throughput, non-destructive and precise method to predict the grain yield of spring barley and could be a suitable tool to deliver information for the brewing industry and plant breeders.

Effect of water stress and plant density on canopy temperature, yield components and protein concentration of red bean (Phaseolus vulgaris L. cv. Akhtar)

In order to study the effects of different irrigation regimes and plant density on yieldcomponent and protein concentration and crop water stress index (CWSI), under research fieldconditions a split plot arrangement was conducted in completely randomized block designduring two years. The first factor of variables was the effects of 4 irrigation levels 120% (I1),100% (I2), 80% (I3) and 60% (I4) of Standard evapotranspiration and the second factor was thespacing of 5 cm (D1), 10 cm (D2) and 15 cm (D3) between plants within a row. Results indicatethat the number of pods in each plant and the grains in each pod decreased when densityincreased, but the hundred-grains-weight and the height of bean plants increased. Whenirrigation increased, various increases were observed in the yield, the number of pods in eachplant, the grains in each pod, the hundred-grains-weight and the height of bean plants, but thegrain protein decreased. Protein concentration increased with more severe water deficit in thesoil. The protein concentration directly correlated with total irrigation water during the growingseason (TI) and the exponential equation P=33* e -4E-04TI can be used for protein concentrationprediction. The effect of the irrigation water was significant and CWSI increased with increasedsoil water deficit. The effect of the density for CWSI was not significant. Grain yield (GY)directly correlated with CWSI and the exponential equation, GY=696.2(CWSI)-0.51 can be usedfor the prediction of grain yield. The CWSI value is useful for evaluating crop water stress inbeans and thus it could be useful in timing the irrigation. Results of this research indicate thatyield components such as height of bean plant, the number of pods in each plant and the numberof grains in each pod were significantly different by applying the different irrigation strategies atthe three different lengths of plant spacing within a row.

Combining ability and heterosis of elite drought-tolerant maize inbred lines evaluated in diverse environments of lowland tropics

Estimates of combining abilities and heterosis of inbred lines are imperative for selection of suitable parents of maize hybrids. This study examined the combining ability of 24 drought-tolerant maize inbred lines, 12 each from International Centre for Maize and Wheat Improvement (CIMMYT) and International Institute of Tropical Agriculture (IITA). The lines were allotted into six groups each comprising four lines. The four lines in one group were used as females and crossed to the four lines in another group as males in six different sets using a North Carolina Design II mating scheme to generate 96 hybrids. The hybrids were evaluated together with four checks across six environments in the rainforest and savannah agro-ecologies of Nigeria between 2011 and 2012. The parental inbred lines were also evaluated in separate trial in each location. Significant hybrids × environment interaction was observed for grain yield and other measured traits. GCA effects accounted for 83.3% of the variation for grain yield at Bagauda, 78.1% at Saminaka, and 77.7% at Ikenne. GCA also contributed 91.1 and 80.0% to the variation observed for plant height and ear aspect, respectively, across the environments. Significant SCA × environment interaction detected for grain yield suggests that hybrids were not stable across test environments. Prediction of grain yield in hybrids using midparent values resulted in a R2 value of 0.13. Midparent heterosis for grain yield varied from 80 to 411%, with the top 36 hybrids recording >200%. Four CIMMYT (EXL02, EXL06, EXL04 and EXL16) and three IITA (ADL33, ADL41, and ADL32) inbred lines had positive and significant GCA effects for grain yield across environments. The novel alleles present in the CIMMYT lines will improve the adapted IITA germplasm in a new population for extracting new set of more productive inbred lines for developing adapted high yielding drought-tolerant maize hybrids.

Physiological basis of maize growth and productivity–A review†

In recent years, major research activities have been directed on the physiological basis of maize growth and productivity. With respect to vegetative growth, researches have been undertaken on leaf development, distribution of oxygen in roots, growth analysis and factors influencing these activities. Besides, several modelling approaches have been undertaken for the prediction of grain yield. With respect to reproductive growth, major attentions have been given on the development of floral meristem, the effect of silk age on fertilisation and pollination. Some studies have been undertaken on transpiration, photosynthesis, oxidative stress, phototropism, tocopherol, water management, drought stress, salinity tolerance, cold tolerance and mineral nutritions. Very few studies have been undertaken for the selection of the genotypes resistant to these stress factors. Among these, a great emphasis has been given on photosynthesis. All these phenomena are directly or indirectly related with maize productivity. There is a great necessity to formulate adequate strategies for managing the crop under adverse agro-climatic conditions and the selection of crop cultivars for adaptation under these adverse situations.

Assessing crop model improvements through comparison of sorghum (sorghum bicolor L. moench) simulation models: A case study of West African varieties

Better defining niches for the photoperiod sensitive sorghum (Sorghum bicolor L. Moench) varieties of West Africa into the local cropping system might help to improve the resilience of food production in the region. In particular, crop models are key tools to assess the growth and development of such varieties against climate and soil variability. In this study, we compared the performance of three process-based crop models (APSIM, DSSAT and Samara) for prediction of diverse sorghum germplasm having widely varying photoperiod sensitivity (PPS) using detailed growth and development observations from field trials conducted in West Africa semi-arid region. Our results confirmed the capability of each selected model to reproduce growth and development for varieties of diverse sensitivities to photoperiod. Simulated phenology and morphology organs during calibration and validation were within the closet range of measured values with the evaluation of model error statistics (RMSE and R2). With the exception of highly sensitive variety (IS15401), APSIM and Samara estimates indicate the lowest value of RMSE (<7days) against the observed values for phenology events (flowering and maturity) compared to DSSAT model. Across the varieties, there was over-estimation for simulated leaf area index (LAI) while total leaf number (TLN) fitted well with the observed values. Samara estimates were found to be the closet with the lowest RMSE values (<3 leaves for TLN and <1.0m2/m2 for LAI) followed by DSSAT and APSIM respectively. Prediction of grain yield and biomass was less accurate for both calibration and validation. The predictions using APSIM were found to be closest to the observed followed by DSSAT and Samara models respectively. Based on detailed field observations, this study showed that crop models captured well the phenology and leaf development of the photoperiod sensitive (PPS) varieties of West Africa, but failed to estimate accurately partitioning of assimilates during grain filling. APSIM and SAMARA as more mechanistic crop models, have a higher sensitivity of the adjustment of key parameters, notably the specific leaf area for APSIM in low PPS varieties, while SAMARA shows a higher response to parameters changes for high PPS varieties.