Variation In Yield Research Articles

Variation in wheat yield and soil properties at different landscape positions, nutrient sources, and rates in the tropical cereal-based cropping systems of Ethiopia

Variation in wheat yield and soil properties at different landscape positions, nutrient sources, and rates in the tropical cereal-based cropping systems of Ethiopia

Evaluating effectiveness of clonal plant selection of alfalfa (Medicago sativa L.) and sainfoin (Onobrychis viciifolia Scop.) in mixtures: Mean performance and stability in a multi‐environment trial

AbstractAlfalfa (Medicago sativa L.) ‐ grass or alfalfa ‐ sainfoin (Onobrychis viciifolia Scop.) mixtures are commonly used for pastures in western Canada because of their high forage quality and their low risk of causing frothy bloat in grazing animals. However, the proportion of these two legumes declines in mixed forage stands over time. The objective of this study was to evaluate the effectiveness of selecting superior genotypes from clonally propagated alfalfa or sainfoin under plant competition in different growth environments. For this study, around 100 genotypes of each legume were cloned and transplanted into meadow bromegrass (Bromus riparius Relm.) or alfalfa swards at Saskatoon, SK and Lethbridge, AB, Canada in 2017. Genotype‐environment (G x E) interactions of alfalfa and sainfoin genotypes were analysed by an additive main‐effects and multiplicative interaction (AMMI) model. Significant variations in plant height, spring vigour and total dry matter yield (TDM) were observed for both species. Among the measured traits, plant height was a more highly heritable trait (H2 = .16 for alfalfa and H2 = .18 for sainfoin), while TDM was the least heritable (H2 = .08 for alfalfa and H2 = .04 for sainfoin). In the AMMI Analysis of variance for TDM, the genotype explained <12% of the variation for both species, suggesting a direct selection of yield would result in a low genetic gain. The biplot mean performance (Y) x weighted average of absolute scores from the singular value decomposition of the matrix of BLUPs stability index (WAASB) identified several promising genotypes with superior performance and stability across different environments that were selected for producing synthetic lines. However, the synthetic lines of both alfalfa and sainfoin did not consistently exhibit superior performance in mixtures compared to their respective check cultivars.

Assessment of Genetic Variability, Correlation, and Path Coefficient for Yield and Its Contributing Traits in Pigeon Pea [Cajanus cajan (L.) Millspaugh

The experimental material consisted of 155 pigeon pea genotypes sown in a Randomized Block Design (RBD) with three replications during the Kharif, 2023-2024 crop season at the N. E. B. Crop Research Centre of G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand. The genetic variance components, correlation, and path coefficient for seed yield and its components were estimated using these 155 pigeon pea genotypes. The highest phenotypic coefficient of variation (PCV) values (> 20%) were recorded for traits such as the number of pods per plant (26.45%), number of secondary branches per plant (22.72%), and seed yield per plant (20.27%). In contrast, the lowest PCV estimates (< 10%) were found for days to maturity (7.39%) and days to 50% flowering (6.73%). High estimates of the genotypic coefficient of variation (GCV) (> 20%) were recorded only for the number of pods per plant (23.6%). The lowest GCV estimates (< 10%) were observed for hundred seed weight (9.63%), number of seeds per pod (9.55%), plant height (9.49%), pod length (7.18%), days to maturity (6.85%), and days to 50% flowering (6.5%). The hundred seed weight showed a high positive direct effect on seed yield per plant (Genotypic = 0.58, Phenotypic = 0.47) along with a positive and significant correlation (Genotypic correlation coefficient, (rg = 0.599); Phenotypic correlation coefficient, (rp = 0.485)). Based on this investigation, it is evident that exploring genetic variability, correlations, and path analyses provides a more effective approach for selecting superior cultivars for yield and related traits.

Correction of the cracking mechanism ratio in catalytic cracking process to characterize the thermal cracking reaction and realize highly sensitive identification of catalysts

AbstractIn this work, we have specially carried out the catalytic cracking experiments of heavy distillate oil in the high temperature range of 500~700°C. The composition of dry gas generated in the catalytic cracking process was analyzed, with emphasis on the variation of yield of C1 and C2 products. Two cracking mechanism ratios (CMRs) were redefined by replacing the (C1 + C2) products in the traditional definition of CMR with CH4, and the feasibility of using them to characterize the thermal cracking reaction in the catalytic cracking process in the high temperature range was investigated. The results showed that CH4 was more sensitive to temperature than (C1 + C2) and it was feasible and more accurate to use CH4 instead of (C1 + C2) corrected R3 to characterize the thermal cracking reaction in the catalytic cracking process in the high temperature range. In addition, it was found that the corrected R3 had the effect of distinguishing and identifying catalysts.

Probabilistic analysis of drought impact on wheat yield and climate change implications

Drought is projected to intensify under warming climate and will continuously threaten global food security. Assessing the risk of yield loss due to drought is key to developing effective agronomic options for farmers and policymakers. However, little has been known about determining the likelihood of reduced crop yield under different drought conditions and defining thresholds that trigger yield loss at the regional scale in Australia. Here, we estimated the dependence of yield variation on drought conditions and identified drought thresholds for 12 Australia's key wheat producing regions with historical yield data by developing bivariate models based on copula functions. These identified drought thresholds were used to investigate drought statistics under climate change with an ensemble of 36 climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that drought-induced yield loss was region-specific. The drought thresholds leading to the same magnitude of wheat yield reduction were smaller in regions of southern Queensland and larger in Western Australia mainly due to different climate and soil conditions. Drought will be more frequent and affect larger areas under future warming climates. Based on our results, we advocate for more effective crop management options, particularly in regions where wheat yield is vulnerable to drought in Australia. This will mitigate potential drought impacts on crop production and safeguard global food security.

Dissecting the Superior Drivers for the Simultaneous Improvement of Fiber Quality and Yield Under Drought Stress Via Genome-Wide Artificial Introgressions of Gossypium barbadense into Gossypium hirsutum.

Global water scarcity and extreme weather intensify drought stress, significantly reducing cotton yield and quality worldwide. Drought treatments are conducted using a population of chromosome segment substitution lines generated from E22 (G. hirsutum) and 3-79 (G. barbadense) as parental lines either show superior yields or fiber quality under both control and drought conditions. Fourteen datasets, covering 4 yields and 4 quality traits, are compiled and assessed for drought resistance using the drought resistance coefficient (DRC) and membership function value of drought resistance (MFVD). Genome-wide association studies, linkage analysis, and bulked segregant analysis are combined to analyze the DR-related QTL. A total of 121 significant QTL are identified by DRC and MFVD of the 8 traits. CRISPR/Cas9 and virus-induced gene silencing techniques verified DRR1 and DRT1 as pivotal genes in regulating drought resistant of cotton, with hap3-79 exhibiting greater drought resistance than hapE22 concerning DRR1 and DRT1. Moreover, 14 markers with superior yield and fiber quality are selected for drought treatment. This study offers valuable insights into yield and fiber quality variations between G. hirsutum and G. barbadense amid drought, providing crucial theoretical and technological backing for developing cotton varieties resilient to drought, with high yield and superior fiber quality.

Ecosystem Services Assessment and Multi-Scenario Prediction in Liaoning Province from 2000 to 2020

Ecosystem service assessment and prediction play a crucial role in sustainable regional development and resource management. Liaoning Province, as a typical representative of Northeast China, faces rapid development challenges such as urbanization, industrialization, and agricultural modernization. At the same time, there is an urgent need for a deeper understanding of the evolution trends of its ecosystems and their impact on ecosystem services. This study employed the InVEST-Markov-PLUS model to conduct simulated research on the assessment of past and future ecosystem services and multi-scenario predictions in Liaoning Province. Based on the land-use changes in Liaoning Province from 2000 to 2020, the InVEST model was used to evaluate the spatiotemporal variations in carbon storage, soil conservation, and water yield in the ecosystem services from 2000 to 2020. Additionally, the equivalent factor method was employed to calculate the value of ecosystem services in Liaoning Province during the same period. Furthermore, by integrating the PLUS and Markov models with the actual conditions of Liaoning Province, four land-use development scenarios for 2030 were constructed, including natural development, economic priority, ecological protection, and cropland protection. The land-use distribution and the quantities and values of ecosystem services under these scenarios were simulated. The study revealed the following findings： ① From 2000 to 2020, carbon storage and soil retention in Liaoning Province showed an overall increasing trend, whereas water yield exhibited a fluctuating decrease trend initially, followed by an increase and then another decrease. ② Carbon storage and soil retention in Liaoning Province showed higher values in the eastern mountainous areas and western hilly regions, with lower values in the central region. Water yield showed a decreasing trend from east to west. ③ The value of ecosystem services increased from 547.94 billion yuan to 565.53 billion yuan, with a total increase of 17.58 billion yuan during the study period. All four types of services showed an increase, with cultural services experiencing the fastest change. ④ In 2030, carbon storage and soil retention in Liaoning Province decreased in all scenarios except for in the ecological protection scenario. Water yield increased only in the cropland protection scenario, whereas it decreased in the other three scenarios. The value of ecosystem services in the study area increased in all scenarios except for in the economic priority scenario.

Vulnerability assessment of aerosol and climate variability for rice and maize yield using EO datasets for sustainable agriculture over India.

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security.

Assessment of Genetic Variability for Physiological Traits and Yield in Bread Wheat (Triticum aestivum L. em. Thell.)

Assessment of Genetic Variability for Physiological Traits and Yield in Bread Wheat (Triticum aestivum L. em. Thell.)

Application of AMMI and GGE biplot for genotype by environment interaction and yield stability analysis in potato genotypes grown in Dawuro zone, Ethiopia

The performance of most crop genotypes is greatly influenced by the interactions between genotype and environment. Providing information on well-adapted and high-yielding potato genotypes in a given environment is paramount for small-holder farmers to enhance their productivity. The study aimed to understand GEI effect on tuber yield, and identify and select widely or specifically adapted high-yielding potato genotypes for production. Eleven genotypes were evaluated using a randomized complete block design with three replications. The results of the AMMI analysis of variance showed that tuber yield significantly (p ≤ 0.05) influenced genotype-environment interaction. This reveals that genotypes exhibit varying mean performance in tuber yield across different environments. The sum squared results showed that genotype (62.40 %) and environment (26.73 %) were the main contributors to tuber yield variation, while the genotype-environment interaction effect (10.87 %) contributed least to then tuber yield total variation. The AMMI, GGE biplot, and GSI analysis revealed Gudanie and Gorebella as superior genotypes in tuber yield, demonstrating high mean performance across tested environments. Thus, Gudanie and Gorebella have been chosen as the most broadly adaptable genotypes for production in all potato-growing agroecologies in the Dawuro zone. The GGE and AMMI biplot provided the genotype-by-environment interaction association among environments and genotypes.

Evaluation of Rumen Microbes For Milk Fat Yield in Cattle Based on 16S rRNA Amplicon Metagenomic Sequencing

Background: Ruminal microbes support growth, lactation and maintenance of animals by providing energy and protein yielding nutrients. Metagenomics is the study of microbial communities directly in their natural environments such as soil, water, gut samples etc. The 16S rRNA gene sequencing is one of the basic genetic markers for the characterisation of prokaryote microorganism community. Current study exploited complex gut microbial communities present in cattle rumen based on variation in milk fat yield. Methods: The study was conducted in cattle reared at University Livestock Farm, Kerala Veterinary and Animal Sciences University. Twelve healthy cattle in the initial stages of lactation (less than three months) were selected and divided into two groups of six each based on high and low milk fat composition. Rumen samples were collected and DNA was isolated. Amplicons of 16S rRNA has been generated from metagenomic DNA targeting V3 and V4 hyper variable regions. Qiime2 was used for analysis for 16S rRNA amplicon sequencing. Result: Results revealed that a total of 38,28,241 sequences were obtained after demultiplexing. Majority of sequence length ranged around 295 to 301 nucleotides for forward reads and 216 to 300 nucleotides for reverse reads. After denoising, a total of 6,62,498 sequences were obtained. Clustering (de-replication) produced 711 unique sequence features. In kingdom level of taxonomy, for bacterial kingdom, there was no significant difference in abundance of organisms for high milk fat yield and low milk fat yield cows. For archaea, significant difference observed between abundance of high milk fat and low milk fat groups at five per cent level in the current study.

Genetic variability, diversity, and character association for dry fruit yield and quality in chili

ABSTRACT Despite the high nutritional value and extensive consumer preference for chili, productivity levels in India are significantly low. An experiment was carried out to study the variability, genetic diversity, and character association in 30 genotypes. Analysis of variance showed significant differences among genotypes for all the traits under study. A high magnitude of genotypic and phenotypic coefficient of variation, coupled with heritability and genetic advance, was observed for all the traits, except days to first flowering, days to first fruit set, and days to final harvest. Diversity analysis showed that the maximum intercluster distance was observed between clusters IX and XII. Results of correlation and path coefficient analysis indicated that number of fruits per plants, fruit length, fruit weight, green fruit yield per plant, number of seeds per fruit, days to first flowering, and days to final harvest were positively correlated with dry fruit yield. It is concluded that improvement in fruit yield in chili pepper could be achieved by selecting genotypes from clusters IX and XII as parents for hybridization, and traits as components that have positive effect on yield.

Genetic variability and trait associations in bread wheat (Triticum aestivum L.) genotypes under drought-stressed and well-watered conditions

Drought is one of the most important yield-limiting abiotic stresses threatening wheat production and productivity. Development of wheat genotypes with enhanced grain yield under drought-stressed conditions depend on the extent of genetic variation present for drought tolerance-related traits. This study was carried out to determine the level of genetic variation and associations of yield and yield attributing traits among 196 bread wheat genotypes under drought-stressed and well-watered conditions. The genotypes were evaluated under greenhouse and field conditions and phenotyped for yield and yield-related traits. The genotypes varied significantly for the traits under both conditions. Grain yield ranged from 2.13 to 3.74 t ha−1 and from 2.52 to 5.06 t ha−1 under drought-stressed and well-watered conditions, respectively. Under both conditions, variances due to genotype were higher than environment and genotype × environment interaction variances for all the traits. Estimates of phenotypic coefficient of variation (PCV) were higher than genotypic coefficient of variation (GCV) for all the traits under both conditions, with both PCV and GCV being highest for kernel weight spike−1 and lowest for spike fertility. Under both conditions, broad-sense heritability estimates ranged from moderately high to very high, accompanied with high genetic advance as a percentage of the mean. Fertile spikelets spike−1, one thousand kernel weight, kernel weight spike−1, biomass yield and harvest index manifested high positive genotypic and phenotypic correlations and positive direct effects on grain yield under both conditions. The first five principal components accounted for 78.4% (well-watered) and 76.0% (drought-stressed) of the variation among the genotypes. The 196 genotypes were delineated into six major clusters under both water conditions, with clusters 3 (well-watered) and cluster 5 (drought-stressed) containing genotypes with the highest ability to tolerate drought stress. Genotypes Alidoro, Bolo, Dinknesh, ETBW8491 and ETBW172938 had high stable yields under both conditions. The identified traits and genotypes were drought tolerant and could be exploited to develop novel genotypes for drought stress tolerance.

Relationship between dry matter accumulation and maize yield in Southwest China

AbstractTo explore the differences in dry matter accumulation and yield of maize varieties having different nitrogen‐use efficiencies in Southwest China, a field experiment was conducted in Yongchuan, Chongqing, and Deyang, Sichuan, from 2019 to 2020. Two varieties, the nitrogen‐efficient Zhenghong 311 (ZH 311) and the nitrogen‐inefficient Xianyu 508 (XY 508), were tested across four nitrogen levels (0–360 kg ha−1). The results showed that compared to XY 508, ZH 311 exhibited a significantly higher accumulation of dry matter at various stages and periods, particularly in the roots during the R6 stage, and in the stem sheaths and leaves throughout all stages. Furthermore, the number of kernel rows, number of kernels per row, number of kernels per ear, and grain yield were significantly higher for ZH 311 than XY 508, whereas the 100‐grain weight was significantly lower for ZH 311 than XY 508. The yield difference between the two varieties was the largest when the nitrogen application rate was 240 kg ha−1. The yield performance of ZH 311 was always better than that of XY 508, and less nitrogen was needed to obtain the best yield. The accumulation of maize dry matter had a highly significant effect on the number of kernel rows, kernels per row, and kernels per ear, and grain yield. The direct effect of the number of kernels per ear on grain yield was very low. However, it affected grain yield through the number of kernel rows and kernels per row. The dry matter accumulation of V6−V12 and R3−R6 contributed the most to grain yield, while in vegetative organs, the effect of leaf dry matter accumulation and yield was the greatest. This investigation will provide insights into factors affecting variations in maize yield under low nitrogen conditions and offer guidance for N‐fertilizer management strategies.

Responses of rice qualities to temperature and light in three different ecological environments in karst regions

Rice cultivation in Karst regions shows significant variations in both yield and quality due to temperature and light. Herein, 241 varieties including 198 indica rice and 43 japonica rice were planted in three ecological regions to investigate responses of rice qualities to meteorological factors. Results showed that in regions with higher average temperature (25.43 °C) and less daily sunshine hour (2.53 h/day), the appearance qualities (chalkiness degree, chalkiness rate and size) and processing qualities (brown rice percentage, head rice and milled rice percentages) were significantly reduced (p < 0.05). A total of 723 samples in three ecological environments were grouped into 4 clusters, arranged in descending order of cluster scores. In high-scoring cluster, the processing quality, appearance quality, protein content, hot paste viscosity, consistence viscosity, and setback viscosity were negatively correlated with temperatures (average temperature, temperature difference, effective accumulated temperature, maximum and minimum temperatures) from heading to mature stages, but positively correlated with sunshine hours. Apparent amylose contents were negatively correlated with temperatures but not significantly correlated with sunshine hours. In low-scoring cluster, only the processing quality had significant correlations with meteorological factors. These findings revealed the interaction between temperature and light for the formation of rice quality.

Exploring strategies for agricultural sustainability in super hybrid rice using the food-carbon-nitrogen-water-energy-profit nexus framework

The breakthrough in super hybrid rice yield has significantly contributed to China's and global food security. However, the inherent conflict between high productivity and environmentally sustainable agriculture poses challenges. Issues like water scarcity, energy crises, escalating greenhouse gas emissions, and diminishing farm profitability all threaten agricultural sustainability. In response to these challenges, we applied a holistic food-carbon-nitrogen-water-energy-profit (FCNWEP) nexus framework to comprehensively evaluate sustainability of distinct crop management strategies across three sub-sites in central China. Field experiments were conducted in Hubei and Hunan Provinces from 2017 to 2021, with a widely adopted elite super hybrid rice (Y-liangyou900). Four crop management treatments were implemented: a control (CK, 0 kg N ha-1), conventional crop management (CCM, 210-250 kg N ha-1, 7:3 basal: mid-tiller fertilizer ratio), and two integrated crop management treatments (ICM1, 180-210 kg N ha-1, 5:2:3 basal: mid-tiller: panicle initiation fertilizer ratio; ICM2, 240-270 kg N ha-1, 5:2:2:1 basal: mid-tiller: panicle initiation: flowering fertilizer ratio). Grain yield, carbon footprint, nitrogen footprint, energy footprint, nitrogen use efficiency and economic benefits were among the assessed variables. Our results showed that significant yield variations were observed, with ICM2 consistently outperforming CCM and ICM1 across the three sites. In Jingzhou, Suizhou, and Changsha, ICM2’s grain yield was 30.2, 24.7, and 13.3% higher than CCM, respectively. Additionally, net profits for ICM2 exceeded those of CCM and ICM1 by 31.8 and 115.2% in Jingzhou, 32.2 and 109.9% in Suizhou, and 15.4 and 34.0% in Changsha. Integrated crop managements, specifically ICM2, demonstrated improved nitrogen and energy use efficiency, resulting in reduced carbon, nitrogen, water, and energy footprints. Overall, composite sustainability scores, calculated using the FCNWEP framework, indicated that both ICM2 and ICM1 generally exhibited higher sustainability levels compared to CCM. This study offers valuable insights into practical management methodologies and provides recommendations for enhancing agricultural sustainability.

Assessing Maize Yield Spatiotemporal Variability Using Unmanned Aerial Vehicles and Machine Learning

Optimizing the prediction of maize (Zea mays L.) yields in smallholder farming systems enhances crop management and thus contributes to reducing hunger and achieving one of the Sustainable Development Goals (SDG 2—zero hunger). This research investigated the capability of unmanned aerial vehicle (UAV)-derived data and machine learning algorithms to estimate maize yield and evaluate its spatiotemporal variability through the phenological cycle of the crop in Bronkhorstspruit, South Africa, where UAV data collection took over four dates (pre-flowering, flowering, grain filling, and maturity). The five spectral bands (red, green, blue, near-infrared, and red-edge) of the UAV data, vegetation indices, and grey-level co-occurrence matrix textural features were computed from the bands. Feature selection relied on the correlation between these features and the measured maize yield to estimate maize yield at each growth period. Crop yield prediction was then conducted using our machine learning (ML) regression models, including Random Forest, Gradient Boosting (GradBoost), Categorical Boosting, and Extreme Gradient Boosting. The GradBoost regression showed the best overall model accuracy with R2 ranging from 0.05 to 0.67 and root mean square error from 1.93 to 2.9 t/ha. The yield variability across the growing season indicated that overall higher yield values were predicted in the grain-filling and mature growth stages for both maize fields. An analysis of variance using Welch’s test indicated statistically significant differences in maize yields from the pre-flowering to mature growing stages of the crop (p-value &lt; 0.01). These findings show the utility of UAV data and advanced modelling in detecting yield variations across space and time within smallholder farming environments. Assessing the spatiotemporal variability of maize yields in such environments accurately and timely improves decision-making, essential for ensuring sustainable crop production.

Estimating Global Wheat Yields at 4 km Resolution during 1982–2020 by a Spatiotemporal Transferable Method

Reliable and spatially explicit information on global crop yield has paramount implications for food security and agricultural sustainability. However, most previous yield estimates are either coarse-resolution in both space and time or are based on limited studied areas. Here, we developed a transferable approach to estimate 4 km global wheat yields and provide the related product from 1982 to 2020 (GlobalWheatYield4km). A spectra–phenology integration method was firstly proposed to identify spatial distributions of spring and winter wheat, followed by choosing the optimal yield prediction model at 4 km grid scale, with openly accessible data, including subnational-level census data covering ~11,000 political units. Finally, the optimal models were transferred at both spatial and temporal scales to obtain a consistent yield dataset product. The results showed that GlobalWheatYield4km captured 82% of yield variations with an RMSE of 619.8 kg/ha, indicating good temporal consistency (r and nRMSE ranging from 0.4 to 0.8 and 13.7% to 37.9%) with the observed yields across all subnational regions covering 40 years. In addition, our dataset generally had a higher accuracy (R2 = 0.71) as compared with the Spatial Production Allocation Model (SPAM) (R2 = 0.49). The method proposed for the global yield estimate would be applicable to other crops and other areas during other years, and our GlobalWheatYield4km dataset will play important roles in agro-ecosystem modeling and climate impact and adaptation assessment over larger spatial extents.

Assessing drought tolerance in advance wheat genotypes using stress tolerance indices

Wheat, an important crop of Nepal, is significantly affected by drought, leading to severe yield losses. Thus, an experiment was conducted to assess effect of drought on wheat traits and to identify drought resilient genotypes comparing stress tolerance indices. Altogether seventy-two genotypes including checks were evaluated under drought and non-stress condition in an alpha-lattice design with two replications at the research block of National Rice Research Program, Hardinath, Nepal during winter of 2019/20. Analysis of variance revealed significant genotypic differences (p&lt;0.01) in traits such as days to heading, anthesis, and maturity, plant height, flag leaf area, spike length, grains per spike, 1000-grain weight, and grain yield under both conditions. The combined analysis of variance showed that genotype, environment, and their interaction significantly influenced most traits. The environment was the dominant factor, accounting for 86.2% of the variation in grain yield, followed by genotype (9.5%) and genotype-environment interaction (4.3%). Among the nine quantitative traits assessed, grain yield was most severely affected due to drought, experiencing a substantial reduction of 63%. To assess drought tolerance, six indices (TOL, SSI, MP, GMP, HMP, and STI) were calculated based on grain yield data. Most indices identified genotypes NL1373, NL1308, NL1407, BL4868, and BL4947 as highly drought-tolerant. Among the indices, MP, STI, and GMP were the most reliable for measuring stress tolerance due to their strong positive correlation with yield under both conditions. These identified genotypes are promising candidates for breeding programs aimed at developing drought-resilient wheat varieties, thereby enhancing food security in drought-affected regions.

Predicting rice productivity for ground data-sparse regions: A transferable framework and its application to North Korea

The use of observation-dependent methods for crop productivity and food security assessment is challenging in data-sparse regions. This study presents a transferable framework and applies it to North Korea (NK) to assess rice productivity based on climate similarity, transferable machine-learning techniques, and extendable multi-source data. We initially divided the primary phenological stages of rice in the study region and extracted dynamic rice distributions based on Moderate Resolution Imaging Spectroradiometer products and phenological observations. We compared the performances of four representative environmentally driven models (Linear Regression, back-propagation Neural Network, Support Vector Machine, and Random Forest) in simulating rice productivity using an extensive dataset that included multi-angle vegetation monitoring, climate variables, and planting distribution information. The framework integrated an optimal environmentally driven model with agricultural management practices for transferability to predict rice productivity in NK over multiple years. Additionally, two crop growth scenarios (whole growth period (WGP) and seeding–heading period (SHP)) were compared to assess pre-harvest forecasting capabilities and identify dominant factors. Finally, independent datasets from the Food and Agriculture Organization, World Food Program, and Global Gridded Crop Models were used to validate the magnitude and spatial distribution of the predicted results. The results showed that phenological identification based on remote sensing can accurately capture rice growth characteristics and map rice distribution. Random Forest outperformed other models in simulating rice productivity variation, with r-squares of 0.87 and 0.83 in the WGP and SHP, respectively. The solar-induced chlorophyll fluorescence, maximum temperature, and evapotranspiration collectively determined approximately 40 % of the variation in yield simulated using Random Forest. Conversely, planting areas contributed over 42 % of the variation in rice production. Compared to Food and Agriculture Organization statistics, the environmentally driven framework explained 78.72 % and 76.89 % of the production variation and 69.42 % and 71.15 % of the yield variation in NK under the WGP and SHP, respectively. Moreover, the environmental management-driven framework captured over 90 % of the yield variation. The predicted spatial pattern of rice productivity exhibited significant concordance with the World Food Program and Global Gridded Crop Model reports. In summary, the proposed transferable framework for crop productivity assessment contributes to early warnings of production reduction and has the potential for scalability across various crops and data-sparse regions.