Yield Variability Research Articles

Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States

ABSTRACT The application of machine learning in crop yield prediction has gained considerable traction, yet uncertainties persist regarding the impact of the yield trends on these predictions and the differences between the detrending methods. In our study, we utilized extreme gradient boosting (XGBoost) to scrutinize the effects of no trend processing (NTP), input year as a feature (IYF), input average yield as a feature (IAYF), input linear yield as a feature (ILYF), and the global detrending method (GDT) on the yield prediction of maize and soybean in the Midwestern United States. Based on our findings, compared with that of NTP, the incorporation of the yield trend as a predictor in XGBoost significantly improved the accuracy and reduced the uncertainty of the yield prediction. Notably, GDT emerged as a standout performer, significantly reducing the average yield prediction error by 0.091 t/ha for soybean and 0.158 t/ha for maize with respect to NTP, and concurrently improving the determination coefficient (R2) by 20.6% and 19.6% for soybean and maize, respectively. Compared with IYF, IAYF, and ILYF, GDT showed substantial improvements ranging from 3.8% to 12.7% in R2 for soybean and 3.6% to 12.7% for maize. The SHapley Additive ExPlanations (SHAP) framework showed that the enhanced vegetation index (EVI), particularly during the soybean podding and maize dough formation stages, played a crucial role in understanding the variations in interannual yield variability. These findings confirmed the importance of GDT in crop yield prediction via machine learning and could be used to facilitate future advancements in machine learning applications for yield forecasting.

Utilizing Machine Learning Framework to Evaluate the Effect of Climate Change on Maize and Soybean Yield

In recent years, climate change patterns and extreme weather events have adversely affected agricultural production and have raised concerns about its effect on crop yields. These changes can affect the crop yield in many ways including the changes in the length of the growing season, planting and harvest time windows, precipitation amount and frequency, and the growing degree days, etc. So, it is important to analyze the effect of climate change on yield variability for a better understanding of its effect on the crops. This study aims to analyze the historical monthly county-level data for the state of Ohio to quantify maize (Zea mays) and soybean (Glycine max) yield temporal variability and to study the impact of proposed climate change scenarios on the yield. Machine learning algorithms were used to model the effect of monthly temperature and precipitation levels on yield variability, and to study the effect of climate change scenarios on yield. In addition, yield prediction models were integrated with proposed climate change scenarios for higher and lower emissions scenarios to predict the maize and soybean yield for the year 2100. To model the effect of weather parameters on temporal yield variability, the Random Forest model outperformed with RMSE of 0.61 Mt/ha and R2 of 0.73 for maize and with RMSE of 0.21 Mt/ha, R2 of 0.64 for soybean crop. Maximum temperature for July which has a negative correlation with yield was found to be the most dominant parameter for maize yield followed by the precipitation in August, July, and June which have a positive correlation with maize yield. Precipitation in August was found to be the most dominant weather parameter for soybean yield followed by maximum temperature in July. Based on the projected average increase in Ohio’s temperature and precipitation levels alone, maize yield was predicted to drop by 13.2% or 18.5% respectively due to lower emissions and higher emissions climate change scenario of Ohio. Similarly, a 6.64% and a 9.63% drop is expected in the soybean yield by the year 2100 for both lower emissions and higher emissions climate change scenarios. In economic terms based on current commodity values, this is an astounding loss of 254.7 to 369.7 million USD for maize and 535.9 – 751.1 million USD for soybean crop to the state of Ohio.

The Clinical Translation of α-humulene - A Scoping Review.

α-humulene, a sesquiterpene found in essential oils of various plant species, has garnered interest due to its potential therapeutic applications. This scoping review aims to consolidate α-humulene's evidence base, informing clinical translation, and guiding future research directions. A scoping review was conducted of EMBASE, MEDLINE, and PubMed databases up to 14th July 2023. All studies describing original research on α-humulene extraction, as well as pre-clinical and clinical research, were included for review. Three hundred and forty articles were analysed. α-humulene yields ranged from negligible to 60.90% across plant species. In vitro experiments demonstrated cytotoxicity against adenocarcinomas (such as colorectal, pulmonary, breast, prostatic, lung, and ovarian), with varying responses in other cell models. Mechanistic insights revealed its involvement in mitochondrial dysfunction, diminished intracellular glutathione levels, and the induction of oxidative stress. In rodent studies, oral administration of α-humulene at 50 mg/kg reduced inflammation markers in paw oedema and ovalbumin-induced airway inflammation. Intraperitoneal administration of α-humulene (50 - 200 mg/kg) exhibited cannabimimetic properties through cannabinoid 1 and adenosine A2a receptors. α-humulene also exhibited a multitude of properties with potential scope for therapeutic utilisation. However, there is a paucity of studies that have successfully translated this research into clinical populations with the associated disease. Potential barriers to clinical translation were identified, including yield variability, limited isolation studies, and challenges associated with terpene bioavailability. Consequently, rigorous pharmacokinetic studies and further mechanistic investigations are warranted to effectively uncover the potential of α-humulene.

Re-evaluating soil moisture-based drought criteria for rainfed crops in peninsular India

Background: Peninsular India, being completely under the influence of monsoonal climate, suffers crop yield variability due to rainfall distribution-induced soil moisture constraints. Timely and appropriate assessment of this rainfall and soil moisture-induced crop yield variability serves as a key for exemplary relief assistance. Per cent available soil moisture (PASM) is one among several drought declaration indices followed by stakeholders in India for declaration of drought, needs re-evaluation as the existing criteria in unable to capture the yield loss due to ineffective classification of PASM categories. This study attempts to revise the agricultural drought classes by PASM based on relationships established between yield of major rainfed crops of the study region and PASM.Methods: Analysis of yield variability due to PASM was carried out based on long term observations in experiments conducted at five dry farming locations (Akola, Parbhani, Kovilpatti, Ananthapuramu and Bengaluru) of peninsular India. The average yield for each category of PASM was calculated and tabulated for regression analysis. The PASM versus yield in each group was correlated and regression equations were developed if significant positive correlations were established.Results: The range of available soil moisture to obtain at least 50 percent of optimum yield in cereals (maize: 26 and finger millet: 52.9 PASM), pulses (pigeon pea: 37.2 PASM), oilseeds (soybean: 26.8 to 30.5, groundnut: 53.8 to 61.7 PASM) and commercial crops (cotton: 26.3 PASM) was 26–61 percent.Conclusion: The revised PASM-based drought classes (0–50 severe; 51–75 mild and 76–100 no drought) would help in drought declaration and precise identification of drought-hit areas for meaningful relief assistance. However, there is further investigation is needed to include a soil component for further fine-tuning of the criteria.

Winegrape Yield and Revenue Variability in Australia

While winegrowers usually want to achieve consistent yield targets, there is a high degree of yield and price (and hence gross revenue) variability in winegrape production. The aim of this study was to determine whether there are differences in yield and revenue variability across climates, varieties, and regions in Australia. This was performed by estimating statistical models of the impact of these three variables on the coefficient of variation of yield and gross revenue per hectare. The results suggest that hotter and drier regions exhibit lower interannual yield variability, something that in the past may have been largely explained by the use of irrigation, but which may change in the future with climate change and higher water prices. The results also showed that there are sometimes differences in yield and revenue variability, not only across regions, but also between varieties.

Shifts in controls and abundance of particulate and mineral-associated organic matter fractions among subfield yield stability zones

Abstract. Spatiotemporal yield heterogeneity presents a significant challenge to agricultural sustainability efforts and can strain the economic viability of farming operations. Increasing soil organic matter (SOM) has been associated with increased crop productivity, as well as the mitigation of yield variability across time and space. Observations at the regional scale have indicated decreases in yield variability with increasing SOM. However, the mechanisms by which this variability is reduced remain poorly understood, especially at the farm scale. To better understand the relationship between SOM and yield heterogeneity, we examined its distribution between particulate organic matter (POM) and mineral-associated organic matter (MAOM) at the subfield scale within nine farms located in the central United States. We expected that the highest SOM concentrations would be found in stable, high-yielding zones and that the SOM pool in these areas would have a higher proportion of POM relative to other areas in the field. In contrast to our predictions, we found that unstable yield areas had significantly higher SOM than stable yield areas and that there was no significant difference in the relative contribution of POM to total SOM across different yield stability zones. Our results further indicate that MAOM abundance was primarily explained by interactions between crop productivity and edaphic properties such as texture, which varied amongst stability zones. However, we were unable to link POM abundance to soil properties or cropping system characteristics. Instead, we posit that POM dynamics in these systems may be controlled by differences in decomposition patterns between stable and unstable yield zones. Our results show that, at the subfield scale, increasing SOM may not directly confer increased yield stability. Instead, in fields with high spatiotemporal yield heterogeneity, SOM stocks may be determined by interactive effects of topography, weather, and soil characteristics on crop productivity and SOM decomposition. These findings suggest that POM has the potential to be a useful indicator of yield stability, with higher POM stocks in unstable zones, and highlights the need to consider these factors during soil sampling campaigns, especially when attempting to quantify farm-scale soil C stocks.

Exploring the genetic variability in yield, nutritional and digestibility traits in oat grains through ruminant nutrition

Oat is a dual-purpose crop used for both food and feed for animals. The objective of this work is to characterize oat varieties for their genetic diversity in yield, physical traits, and nutritional composition, aiming to identify potential parent varieties for breeding programs to develop new oat varieties for improved livestock feed and diverse industrial applications. To conduct, chemical analysis for protein and carbohydare fractions, energy and digestible nutrient estimated, stastical analyses performed to assess genetic variations for traits among vaieties. Significant genetic variation (p < 0.05) for grain yield, grain density, sieving percentage, crude protein, ether extract, neutral and acid detergent fiber, cellulose, lignin, neutral and acid detergent insoluble nitrogen were observed in grains of eight oat varieties. All protein fractions exhibited significant differences (p < 0.05). Total carbohydrate content ranged significantly (p < 0.05) from 73 % to 79 %. The grains contained higher levels of intermediately degradable starch and pectin (54.12–60.16 %) compared to the slowly degradable cell wall (26–33 %), lignin bounded cell wall (6–10 %), and rapidly degradable sugars (2–8%). Significant variation (p < 0.05) was observed in terms of gross energy, digestible energy, metabolizable energy, net energy for maintenance and lactation about (2 Mcal/kg dry matter), gain (1.6–1.8 Mcal/kg dry matter), total digestible nutrients, digestible dry matter, rumen degradable protein, and total digestible nutrients related to crude protein, fatty acid, neutral detergent fiber, and non-fiber carbohydrate. Organic matter and ether extract were positively associated (p < 0.01) with total digestible nutrients, digestible and metabolizable energy, dry matter digestible and truly digestible non fibrous cabohydrates, while neutral and acid detergent fiber and cellulose showed negative correlation. The research shows that oat varieties vary widely in their yield, physical features, and nutritional content, offering potential for breeding better varieties for both animal feed and industrial uses.

Análise espacial da produtividade da cenoura irrigada em função das propriedades físicas do solo

ABSTRACT Understanding soil, plant attributes, and carrot crop dynamics is vital for optimizing site-specific management practices and enhancing crop yield and soil quality. This study aimed to ascertain the dependence and spatial variability between irrigated carrot yield and soil physical attributes in a clayey Oxisol. The research took place during the 2021 growing season in a commercial carrot production area in Campos Altos, MG, Brazil. One hundred sampling points were established within the irrigated area, distributed across a square expanse of 40,000 m², positioned at the centers of 20 × 20 m grids. The study revealed high yield in the irrigated carrot crop (99.74 t ha-1) with moderate variability. Soil attributes displayed a range of heterogeneity, ranging from low to extremely high, contingent on the variable assessed, as determined by the coefficient of variation. Among the variables exhibiting spatial dependence, particle density exhibited a minimum range of 40 m, which is recommended for further studies on irrigated carrot cultivation in a clayey Oxisol. Total soil water availability demonstrated a positive spatial correlation and proved to be the recommended variable for estimating the spatial variability of carrot crop yield.

Effect of No Tillage and Conventional Tillage on Wheat Grain Yield Variability: A Review

Conservation Agriculture (CA) covers more than 205 million hectares in the world. This made it possible to face and mitigate the challenges of climate change, reducing soil erosion and providing multiple ecosystem services. The first elementary factor influenced is the yield evaluation. It has a direct effect on farmers’ choices for sustainable production. The present article records a review focused on wheat yield average positive change compared between conventional tillage (CT) and no tillage (NT) systems. The international database collected showed that NT is adaptable everywhere. The results of wheat yield differentiation showed the influence of crop rotation depending on stations located in different climatic zones. In more than 40 years of research, specialists have succeeded in demonstrating the importance of crop productivity like wheat. The whole integrates also experimentations where the initiation starts more than ten years.

Large scale water yield assessment for sparsely monitored river basins: A case study for Afghanistan

This paper presents results from a study on water yield assessment across five major river basins of Afghanistan. The study was conducted using GR4J and GR4JSG precipitation-runoff models. The river basins were divided into 207 subcatchments and each subcatchment was divided into multiple functional units. The model was calibrated using observed streamflow data from 2008 to 2015 and validated over the 2016 to 2020 period. Model parameters were calibrated for an unregulated subcatchment in each basin and calibrated parameters from the best-performing subcatchment were transferred to other subcatchments. Results show that modelled water yield across the five basins varies from 0.3 mm in the Helmand basin to 248 mm in the Panj-Amu basin, with an average of 72.1 mm for the entire country. In the period of 2008 to 2020, area averaged water yield in the five basins varies from 36 to 174 mm. For the same period, mean annual precipitation for the entire country is 234.0 mm, indicating a water yield of 30.8%. The nation-wide average water yield of 72.1 mm is equivalent to 46.3 billion cubic meters (BCM) of surface water for the country. In addition, about 28.9 BCM generates annually in the neighbouring Tajikistan and Pakistan from snow and glaciers of the Hindu-Kush mountains. The elevated northern parts of Afghanistan, including parts of neighbouring Tajikistan are the primary water source. Water yield across the country varies between years but there is no consistent increasing or decreasing trends. About 60 to 70% of flow occurs between March to June. The study identified the high water yield areas and investigated variability at monthly, seasonal, and annual time scales. An importance finding is the large spatial and temporal variability of water yield across the basins. This information is crucial for long-term water resources planning and management for agricultural development.

Teff grass increases summer forage availability in the Rolling Plains of Texas

AbstractTeff [Eragrostis tef (Zucc.) Trotter] grass has gained popularity in the United States as an emergency summer forage for livestock due to its drought tolerance and rapid growth habit. An alternative forage would benefit Texas Rolling Plains growers due to the summer forage shortage caused by drought conditions. However, among the teff research conducted in the United States, there is great variability in the forage yields and nutritive values depending on the amount of nitrogen and available water. The objective of the study was to evaluate forage productivity and nutritive value of teff in the Texas Rolling Plains under irrigated and dryland conditions with two nitrogen rates and application timings. Nitrogen fertilizer treatments included single applications of either 50 or 100 lb a−1 applied at planting and a split application of either 50 or 100 lb a−1 applied both at planting and after the first harvest. No differences were observed among nitrogen rates and application methods in the irrigated trial (5286 lb a−1). The split application of 100 lb a−1 produced the greatest total yield in the dryland trial (3096 lb a−1) with no yield advantage of 100 lb N a−1 over 50 lb N a−1 at the first cutting. Forage nutritive values were similar to the warm‐season forage species utilized in the region. This study showed that teff can be an alternative forage species in the region. Future research will include planting dates, nitrogen application timing, cutting height, and the interaction of these factors on yield and nutritive values of teff.

Use of Eucalyptus Charcoal Waste in the Formulation of Substrate for the Cultivation of Two Strains (LED 20/11 and LED 20/12) of Lentinula edodes

The shiitake mushroom (Lentinula edodes) is globally valued for its nutrition and medicinal properties. New technologies aim to increase production with less environmental impact, considering materials such as charcoal for substrate enrichment. This manuscript evaluated the effect of fine charcoal (FC) on the substrate formulation of two L. edodes strains (LED 20/11 and LED 20/12). The substrate consisted of 72% eucalyptus sawdust, 12.5% rice bran, 12.5% wheat bran, and 3% calcium carbonate (control treatment without charcoal). Treatments with FC proportionally reduced the use of sawdust, with doses of 1%, 2%, 4%, 8%, and 16% (relative to the substrate material). Yield, mushroom number, and mushroom weight were evaluated. The concentration of FC significantly affected the parameters analyzed, especially at the 4% dose. A negative correlation between mushroom number and weight was observed. For yield, the control treatment and the lowest dose of FC (1%) had the highest yields for the first harvest. Strain LED 20/12 showed lower yield variability due to the percentage of FC applied to the substrate. The incorporation of FC into the substrate for shiitake cultivation demonstrates efficacy; however, both the concentration and strain used are limiting factors for its applicability.

Comparative analysis of thermal indices for modeling cold and heat stress in US dairy systems

Quantifying the impact of thermal stress on milk yields is essential to effectively manage present and future risks in dairy systems. Despite the existence of numerous heat indices designed to communicate stress thresholds, little information is available regarding the accuracy of different indices in estimating milk yield losses from both cold and heat stress at large spatio-temporal scales. To address this gap, we comparatively analyzed the performance of existing thermal indices in capturing US milk yield response to both cold and heat stress at the national scale. We selected four commonly used thermal indices: the Temperature and Humidity Index (THI), Black Globe Humidity Index (BGHI), Adjusted Temperature and Humidity Index (THIadj), and Comprehensive Climate Index (CCI). Using a statistical panel regression model with observational and reanalysis weather data from 1981-2020, we systematically compared the patterns of yield sensitivities and statistical performance of the four indices. We found that the US state-level milk yield variability was better explained by the THIadj and CCI, which combine the effects of temperature, humidity, wind, and solar radiation. Our analysis also reveals a continuous and nonlinear responses of milk yields to a range of cold to heat stress across all four indices. This implies that solely relying on fixed thresholds of these indices to model milk yield changes may be insufficient to capture cumulative thermal stress. Cold extremes reduced milk yields comparably to those impacted by heat extremes on the national scale. Additionally, we found large spatial variability in milk yield sensitivities, implying further limitations to the use of fixed thresholds across locations. Moreover, we found decreased yield sensitivity to thermal stress in the most recent two decades, suggesting adaptive changes in management to reduce weather-related risks.

Identification of Plant and Soil Characteristics Affecting Stability of Winter Wheat Cultivar in Temperate Climates

This study examines the significant variability in grain yield, thousand-grain weight, protein content, sedimentation value, and falling number among winter wheat cultivars across diverse trial locations, elucidating the profound influence of environmental factors on these traits. Employing Shukla’s stability variance and a multi-trait stability index (MTSI), cultivar stability is comprehensively assessed across multiple traits. Cultivars are ranked based on stability variance and cumulative ranking across all traits, with Bataja emerging as the most stable cultivar according to Shukla variance, while Apostel exhibits the lowest stability. Contrarily, MTSI rankings reveal distinct top performers, such as Medalistika and KWS Spencer. Canonical correspondence analysis (CCA) is utilized to discern relationships between stability and genotype characteristics, as well as trait values and soil properties/weather conditions. These findings contribute to the recommendation of stable cultivars for breeding programs and the optimization of crop management practices. Furthermore, this study underscores the need to explore causal relationships between accompanying variables, facilitating informed recommendations for plant breeders and advancing breeding progress amidst a changing climate. The use of multivariate statistical methods, including CCA, enhances our understanding of cultivar traits and stability, offering valuable insights for sustainable agriculture.

Dissecting Genotype by Environment Interactions in Moroccan Wheat: An Advanced Biplot and Heatmap Analysis Unveiling Agronomic, Quality Traits, and Genotypic Stability for Tailored Breeding Strategies.

This five-year study (2016-2021) across diverse Moroccan agro-climatic zones investigated genotype by environment (G × E) interactions in wheat, focusing on variations in agronomic traits and quality attributes such as protein and gluten content. Significant environmental effects were observed on key traits, like yield, thousand kernel weight (TKW), and spikes per square meter (Spk/m2), highlighting environmental factors' role in wheat yield variability. In the Tassaout (TST) location, notable genotypic effects emerged for traits like biomass, underscoring genetic factors' importance in specific contexts, while in Sidi El Aidi (SEA) and Marchouch (MCH), genotypic effects on yield and its components were predominantly absent, indicating a more substantial environmental influence. These findings illustrate the complexity of G × E interactions and the need for breeding strategies considering genetic potential and environmental adaptability, especially given the trade-offs between yield enhancement and quality maintenance. Insights from the biplot and heatmap analyses enhanced the understanding of genotypes' dynamic interactions with environmental factors, establishing a basis for strategic genotype selection and management to optimize wheat yield and quality. This research contributes to sustainable wheat breeding in Morocco, aligning with global efforts to adapt wheat breeding strategies to changing climatic conditions.

Growth and yield variability of wheat as effected by split application of potassium and nitrogen

Growth and yield variability of wheat as effected by split application of potassium and nitrogen

Environmental drivers of wheat yield variability across China's production regions: Insights from field experiments

AbstractWheat crops underpin contemporary global food security. Predominant wheat production zones in China include the Huang‐Huai‐Hai‐Plain and the Mid‐Lower Reaches of the Yangtze River, although climatic effects on productive potential across these regions vary markedly in space and time. Here, we conducted field experiments during the wheat season of 2015–2018 to examine environmental effects on growth, with fertilization and irrigation provided at levels ensuring that nutrient and water stress exposure was minimal. Yields in Huang‐Huai‐Hai‐Plain and the Mid‐Lower Reaches of the Yangtze River averaged 8950 and 4818 kg ha−1, respectively. Yield variation across regions was primarily related to spike number per unit area and grain number per spike. Maturity biomass was higher in Huang‐Huai‐Hai‐Plain; this translated into higher grain yields. Lower temperature and longer growing duration between emergence and jointing in Huang‐Huai‐Hai‐Plain afforded higher tillering and spike numbers, whereas higher growth rates from jointing to maturity resulted in higher biomass production in Huang‐Huai‐Hai‐Plain compare with the Mid‐Lower Reaches of Yangtze River. Growth rate, grain numbers and yield were positively correlated with the ratio of daily intercepted solar radiation to mean temperature during jointing to anthesis, termed photothermal quotient. Collectively, our results suggest that growth rate accounted for more variation in biomass production compared with growth duration, and the photothermal conditions in the Mid‐Lower Reaches of the Yangtze River were restrictive for spike development and yield formation. Our results help disentangle drivers of crop growth through the development of agro‐environmental conceptual frameworks, enabling a better understanding of yield variability in space and time.

An insight into variability and association among yield and its components under alkalinity and inland salinity stress in rice (Oryza sativa L.)

An experiment was conducted to study the variability, correlation, direct and indirect effects of yield and its attributing traits in 36 genotypes and to identify suitable parents for breeding of alkalinity and inland salinity stress tolerant lines at ARS, Kampasagar, Nalgonda during rabi, 2021-22. Analysis of variance revealed plenteous quantum of variation among the tested genotypes. Wide variation, high PCV, GCV, heritability and genetic advance were observed for seedling mortality, sterility percentage and number of filled grains per panicle. Number of productive tillers/hill and number of filled grains per panicle had a positive and significant correlation with yield whereas seedling mortality and sterility percentage had a negative and significant association with yield. Path analysis inferred that traits such as number of productive tillers/hill, number of grains per panicle and 1000 grain weight had a positive direct effect on yield. Hence,priority should be given to the above mentioned traits to develop alkalinity and inland saline tolerant rice cultivars. Lines CT118911, Sahel177, M202, and KPS10654 were found promising to be utilized as tolerant parents in the breeding programmes.

Screening for Heat and Rain Tolerant Cultivars of Cauliflower for Cultivation in Monsoon Season in the Subtropical Region of Bhutan

There is a shortage of cauliflower in the domestic market during monsoon and early autumn seasons in Bhutan due to the absence of suitable cultivars for cultivation in subtropical region during these seasons. This study was conducted to assess the genotypic variability in yield of different cultivars of cauliflower under heat and rain (flood) stresses in the subtropical region. Four F1 hybrid varieties of cauliflower from the Takii Seeds, Japan (KSB-12.179, KSB-12.180, KSB-18.297 and KSB-20.320) and three F1 hybrid varieties from Syngenta Seeds, India (C-6099, CFL-1522 and Monsoon Queen) were evaluated in August 2022 in open field at ARDC Samtenling. The experiment was laid out in a RCDB design with three replications using Snow Mystique as the check variety. The objective of the study was to identify short duration, rain and heat tolerant varieties of cauliflower for cultivation in the monsoon season (July-August) in the subtropical region of Bhutan. In addition, all the cultivars were further assessed in September, and five cultivars that performed well in monsoon planting were evaluated in November 2022 as the second and third staggered planting trials to find out their performance in late monsoon and main seasons (September-November) respectively. The results from three staggered trials showed that Monsoon Queen matured in 53 days after transplanting (DAT), 56 DAT and 35 DAT in the first, second and third staggered planting trials with the mean head of 462 g, 376 g and 211 g respectively. In comparison, the check variety matured in 142 DAT, 114 DAT and 77 DAT in the first, second and third staggered planting trials with the mean head of 658 g, 403 g and 767 g, respectively. Monsoon Queen was the earliest maturing variety with good head weight and curd quality among the eight cauliflower varieties when transplanted in August 2022. However, both the head yield and curd quality of this cultivar declined in later plantings, indicating that this variety is not suitable for main season (autumn) planting. With the identification of Monsoon Queen as the heat and rain tolerant cultivar in this study, cauliflower can now be grown even in the monsoon season in the subtropical region of Bhutan.

Climate drives of growth, yield and microclimate variability in multistoried coconut plantation in Konkan region of Maharashtra, India

Long term experiments (2013-14 to 2018-19) were conducted in Regional Coconut Research Station, Bhatye, a representative location of major coconut growing region of Maharashtra (Konkan region) to study the impact of changing weather parameters on growth and yield of 32 years old coconut plants (dwarf x tall, i.e., COD x WCT). Regression based trend analysis of weather parameters was conducted to check the variability of weather parameters over experimentation years. There was a decrease in maximum temperature (r2=0.034) and increase in minimum temperature (r2=0.017) and rainfall (r2=0.393), indicating change in weather parameters. Correlation studies were carried out to understand the interaction between weather parameters and coconut growth and yield. Maximum temperature had a negative impact on growth (-0.02 and -0.58 for number of leaves and annual leaf production) but had a positive impact on yield (0.41, 0.64 and 0.63 for number of bunches, number of buttons and nut yield). Minimum temperature had significant negative effect on annual leaf production (-0.88) and had a positive effect on nut yield per plant (0.95). The effect of relative humidity (morning and evening) was non-significant. Rainfall had its influence on the crop by negatively affecting the number of bunches (-0.10) and nut yield per plant (-0.48), a positively affecting number of buttons (0.08). Further, microclimate in the plantation was compared to an open field, which indicated lower maximum and minimum temperature (-3.4 and -3.1 %) and higher morning and evening relative humidity (1.6 and 1.9 %) in the coconut plantation as compared to the open field.