Yield Data Research Articles

Comparing standardised precipitation and barley yield across Quebec.

Climate change and variability continue to affect crop production across the world in general and in Quebec in particular. Therefore, it is important to better understand this climate-yield nexus. Unfortunately, in Quebec, there are currently no studies that analyse both precipitation and barley yield. This study aims at filling this research gap by comparing standardised precipitation (SPI) against barley yield gaps across Quebec for three peripheral and three southern/central regions of Quebec. The study uses growing season precipitation data from Ouranos and barley yield data from Institut de la Statistique du Québec. This work deploys the use of standardised index (SPI)index and a machine learningyield gapalgorithm (actual barley yield minus projected barley yield) to provide a provincial portrait of the relationships between SPI and barley yield. The results show that the peripheral regions record below zero SPIs (Abitibi-Temiscamingue - 0.48, Saguenay-Lac-Saint-Jean - 0.14 and Outaouais - 0.10) and more yield gap years, while the southern/central regions record positive SPIs (Estrie 1.17, Centre du Quebec 0.86 and Monteregie: 0.33) and fewer years with yield gaps. This shows that there is a south-north gradient in the variations of SPI and yield gaps. The SPI and yield patterns can be explained by prolonged winters further north of Quebec and recently winters with insufficient snow cover which triggers rapid snow melt and thus shortening the growing season for barley. Policy actions around drought-resistant varieties within a co-creation context and more research that explores daily and monthly liquid precipitation totals during the growing season of the crop need to be explored. There is also a need to better understand the economic costs and benefits of the associated yield gapsas well as the impacts of temperature.

DEVELOPMENT AND APPLICATION OF AN INTEGRATED INFORMATION MODEL FOR OPTIMIZING LAND USE AND FORECASTING YIELDS IN AGRICULTURAL PRODUCTION

The paper presents the development of a conceptual information model for optimizing land use and crop forecasting in agriculture, implemented using the PHP programming language, Javascript and the MYSQL database management system. The model is designed to solve optimization problems in various fields such as logistics, manufacturing, finance, etc. As part of the work, an information model was developed, which is a set of data, algorithms and software modules designed to solve optimization problems. The model is implemented based on PHP, Javascript, and MYSQL technologies. The model allows you to solve optimization problems of varying complexity using various optimization methods such as linear programming, nonlinear programming, artificial intelligence methods, etc. The model can be applied to solve optimization problems in various fields. In addition, the model allows you to solve optimization problems with a high degree of accuracy and in a short time, and also has a user-friendly interface that allows users to easily and quickly set optimization tasks. An information model has been developed that will make it possible to predict the harvest taking into account data on weather, soil, crops and historical yield data, as well as optimize land use taking into account data on soil type, climate, crop rotation and financial indicators. The developed information model can improve the efficiency and sustainability of agricultural production. The model can be used by agronomists and farmers to make more informed decisions. The model takes into account data on soil type, climate, crop rotation and financial indicators to optimize land use.

On-farm Pre-extension Demonstration of Newly Released Pigeon pea (Cajanus cajan) Technology with Proper Agronomic Practices in Gamo and Konso Zones of Southern Ethiopia

Pigeon pea is widely cultivated by Ethiopian farmers, but its production remains low despite suitable conditions. This is partly due to inadequate demonstration of new pigeon pea varieties with proper agronomic practices. To address this, Arba Minch Agricultural Research Center released a new variety, Ashenafi, which showed promise in adaptation trials. However, its farmer preference and socio-economic benefits were not assessed. Therefore, a pre-extension demonstration of Ashenafi and a local check was conducted in 2024 at Karat Zuria and Boreda districts to improve pigeon pea production. The demonstration covered 20 farmers’ fields and 2 Farmers Training Centers, with 10m x 50m plots for each variety, using a spacing of 1.2m between rows and 0.5m between plants, a seed rate of 3 kg/ha, and a fertilizer rate of 100 kg/ha NPS. Grain yield data and farmer preferences were collected and analyzed. Ashenafi yielded 1,860 kg/ha compared to the local variety's 1,065 kg/ha at Karat Zuria, and 1,785 kg/ha compared to 890 kg/ha at Boreda. Results at the Farmers Training Center were similar. The significant yield gap of 787 kg/ha suggests a need to encourage adoption of the new variety. Ashenafi proved more profitable and was preferred by farmers for its earliness, quick branch emergence, seed size, grain yield, and pod number. Thus, Ashenafi is recommended for wider production with proper agronomic practices.

Digitalising BIPV energy simulation: A cross tool investigation

The long-term viability of Building Integrated Photovoltaics (BIPV) as a renewable energy technology has garnered increased attention in recent discussions. However, there is currently a noticeable absence of simulation tools specific to BIPV applications that can cover the whole modelling chain. It remains unclear to what extent existing PV and BIM-based simulation tools can effectively address the complexities of BIPV projects. Therefore, this study aims to assess the process of existing simulation tools for BIPV energy simulation, three standalone PV tools (SAM, PV*SOL premium, and PVsyst), two Building Information Modelling (BIM)-based standalone PV tools (BIMsolar and Solarius PV), two plug-ins in BIM-based tools (INSIGHT for Revit, Ladybug Tools for Grasshopper/Rhinoceros 3D), and one Computer-Aided Design and Drafting (CADD) tool plugin (Skelion for Sketchup). Based on one existing building project with three different types of BIPV-installations, this study explored the capability of these eight tools in modelling/importing building geometry, selecting weather data, setting system layout and array, evaluating the solar resource, estimating energy losses, and assessing energy generation. The simulation results are compared with monitored energy yield data and presented with deviation analysis. Suggestions focus on pointing out the future development directions for BIPV digital simulation. This study offers insights and guidance for digitalising the BIPV performance simulation in the complex building design.

Radiation safety of ultra-high dose rate electron accelerators for FLASH radiotherapy.

An ultra-high dose rate (UHDR) electron accelerator for FLASH radiotherapy (RT) produces very intense bremsstrahlung by the interaction of the electron beam with objects both inside and outside of the accelerator. The bremsstrahlung dose per pulse is typically 1-2 orders of magnitude larger than that of conventional RT x-ray treatment of the same energy, and for electron energies above 10 MeV, the bremsstrahlung produces substantially more induced radioactivity outside the accelerator than for conventional RT. Therefore, a thorough radiation safety assessment is mandatory prior to the operation of a UHDR electron accelerator. To evaluate the radiation safety of a prototype FLASH-enabled Varian TrueBeam accelerator and to develop a general framework for assessment of all key radiation safety properties of a UHDR electron accelerator for FLASH RT. Production of bremsstrahlung and induced radioactivity by a UHDR electron accelerator is modeled by various analytical methods. The analytical modeling is compared with National Institute of Standards and Technology (NIST) bremsstrahlung yield data as well as measurements of primary bremsstrahlung outside the bunker and induced radioactivity of irradiated thick targets for a FLASH-enabled 16 MeV Varian TrueBeam electron accelerator. In addition, the analytical modeling is complemented by measurements of secondary bremsstrahlung inside/outside the bunker and neutrons at the maze entrance. Calculated bremsstrahlung yields deviate maximum 8.5% from NIST data, and all measurements of primary bremsstrahlung and induced radioactivity agree with calculations, validating the analytical tools. In addition, it is found that scattering foil bremsstrahlung dominates primary bremsstrahlung and the main source of secondary bremsstrahlung is the irradiated object outside the accelerator. It follows that primary and secondary bremsstrahlung outside the bunker can be calculated using the same simple formalism as that used for conventional RT. Measured primary bremsstrahlung tenth-value layers for concrete of the simple formalism are in good agreement with NCRP and IAEA data, while measured secondary bremsstrahlung tenth-value layers for concrete are considerably lower than NCRP and IAEA data. All calculations and measurements form a general framework for assessment of all key radiation safety properties of a UHDR electron accelerator. The FLASH-enabled Varian TrueBeam accelerator is safe for normal operation (max. 99 pulses per irradiation) in a bunker designed for at least 15 MV conventional x-ray treatment unless the UHDR workload is much larger than the x-ray workload. A similar finding applies to other UHDR electron accelerators. However, during beam tuning, radiation survey, or other tests with extended irradiation time, the UHDR workload may become very large, necessitating the implementation of additional safety measures.

Machine learning based on functional principal component analysis to quantify the effects of the main drivers of wheat yields

Assessing the response of crop yield to year-to-year climate variability at the field scale is often done using process-based models and regression techniques. Although powerful, these tools rely on strong assumptions and can lead to substantial prediction errors. In this study, we investigate the use of a flexible machine learning algorithm combining Functional Principal Component Analysis and Random Forest, to relate field scale wheat yield to local daily climate variables. Instead of computing seasonal, monthly or any other arbitrary time-frame climate averages, climate time series are decomposed by Functional Principal Component Analysis into a few data-driven basis functions, called Principal Curves, in order to summarize the dynamic of key climate variables by a limited number of interpretable components. Scores associated to these components are then used as inputs of a Random Forest algorithm for yield prediction and for analysing important factors responsible for yield variability. To evaluate our approach, we use a French national database including wheat yield data as well as climate and management practice data for 298 farm fields from 2011 to 2016 in four main producing regions. Depending on the regions, our approach can explain from 62 % to 81 % of the yield variability when both agronomic and climate variables are included, down to 56–81 % when ignoring agronomic variables and 51–74 % when ignoring climate variables. Based on a year-by-year cross-validation, RMSE ranges from 0.5 t ha−1 to 2.1 t ha−1 in non-extreme years (2012–2015). However, prediction error can reach 3.6 t ha−1 in case of exceptional weather conditions, such as those experienced in 2016 in Northern France. We find that this new approach performs in most cases better than the same machine learning algorithm using the usual time averages of climate variables, without the need to choose an arbitrary time-frame. We then show how important patterns in weather time series can be identified and how their effects on yield can be interpreted using the proposed modelling framework.

Morphological evaluation and yield performance of lentil genotypes against stemphylium blight

Experiment was designed to screen out lentil genotypes against Stemphylium blight (SB). Thirty-five test genotypes along with one moderately susceptible popular local control ‘Moitree’ were grown under natural epiphytotic condition in the farm of BCKV during 2020-21 and 2021-22. Genotypes wise crop phenological characters, yield and disease severity data were recorded. The result revealed, genotypes ILL10838/ILWL11/X2016S was resistant to SB with highest yield (1184.00 kg/ha) followed by 8114/10956/16-1 (1112.20 kg/ha) and IPL 220 (1072.90 kg/ha) with MR reaction. Pooled analysis of two years experimental data used to develop the model Y = 336.21 + 6.12x [R2 = 0.86] explained that there is an increase of yield 6.12 kg/ha with every 1 unit increase in the number of pods per plant. Correlation of determination value (R2 = 0.86) proved the feasibility of the model. Lentil genotypes ILL10838/ ILWL11/X2016S and 8114/10956/16-1 are the best suited for its cultivation and may scaled up for future breeding programme. Bangladesh J. Bot. 53(2): 381-389, 2024 (June)

High-Resolution Yield Mapping for Eucalyptus grandis—A Case Study

Yield data represent a valuable layer for supporting decision-making as they reflect crop management results. Forestry decision-makers often rely on coarse spatial resolution data (e.g., forest inventory plots) despite the availability of modern harvesters that can provide high-resolution forestry yield data. The objectives of this study were to present a method for generating high-resolution Eucalyptus grandis yield data (individual tree-level) and explore their applications, such as correlation analysis with soil attributes to aid nutrient recommendations. Two evaluations were conducted at two sites in Brazil: (a) assessing the positioning accuracy of the global navigation satellite system (GNSS) receiver positioning, and (b) analyzing the yield data and their correlation with the soil attributes. The results indicated that positioning the GNSS receiver at the harvesting head provided higher accuracy than placement at the top of the harvester cabin for individual tree-level data. Reliable yield data were generated despite the GNSS receiver’s increased susceptibility to damage when mounted on a harvest head. The linear correlation analysis between the Eucalyptus grandis yield data and soil attributes showed both negative (Clay, B, S, coarse sand, and potential acidity − H + Al) and positive correlations (K, Mg, pH-SMP, Ca, sum of bases, pH, base saturation, fine sand, total sand, and silt content). This study demonstrates the feasibility of obtaining high-resolution yield data at the individual tree-level and their correlation with soil attributes, providing valuable insights for improving forestry decision-making.

Анализ на зависимостта между общия добив и напоителната норма при царевицата

The purpose of this work is to study the influence of reduced irrigation rates on the productivity of corn (FAO group 600) grown for grain in the Plovdiv region and to establish the parameters of the “Total yieldirrigation rate” relationship. The experiment was carried out in the period 2004-2018 in Agricultural University – Plovdiv, with the late hybrid “Knezha-613”. The variants of the experiment are: 1) without irrigation; 5) optimal irrigation (pre-irrigation soil moisture 75% of FC for the layer 0-80 cm); 2, 3, and 4) irrigation with respectively 25, 50 and 75 % of the rate for variant 5. The relationship is determined based on three types of formulas – regression equation, quadratic formula, and power formula. The highest accuracy is achieved by power relationship at power exponent n=1.3 and r=0.725. The quadratic formula is recommended when relative yield data are only under non-irrigated conditions. These two variants of relationship give a sufficiently accurate forecast for the yield when irrigation rates are reduced by up to 25%. The regression is also usable, but it is inferior to the power and quadratic variants.

Enhanced coalbed methane well production prediction framework utilizing the CNN-BL-MHA approach

As the mechanization of the CBM extraction process advances and geological conditions continuously evolve, the production data from CBM wells is deviating increasingly from linearity, thereby presenting a significant challenge in accurately predicting future gas production from these wells. When it comes to predicting the production of CBM, a single deep-learning model can face several drawbacks such as overfitting, gradient explosion, and gradient disappearance. These issues can ultimately result in insufficient prediction accuracy, making it important to carefully consider the limitations of any given model. It’s impressive to see how advanced technology can enhance the prediction accuracy of CBM. In this paper, the use of a CNN model to extract features from CBM well data and combine it with Bi-LSTM and a Multi-Head Attention mechanism to construct a production prediction model for CBM wells—the CNN-BL-MHA model—is fascinating. It is even more exciting that predictions of gas production for experimental wells can be conducted using production data from Wells W1 and W2 as the model’s database. We compared and analyzed the prediction results obtained from the CNN-BL-MHA model we constructed with those from single models like ARIMA, LSTM, MLP, and GRU. The results show that the CNN-BL-MHA model proposed in the study has shown promising results in improving the accuracy of gas production prediction for CBM wells. It’s also impressive that this model demonstrated super stability, which is essential for reliable predictions. Compared to the single deep learning model used in this study, its prediction accuracy can be improved up to 35%, and the prediction results match the actual yield data with lower error.

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<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.

VSG-LFC on multi-area power system under unsecured communication channel using hybrid trigger mechanism

This paper studies the frequency fluctuation problems of a multi-area power system with renewable energy sources (RESs) under denial-of-service (DoS) attacks. (i) Due to the decoupling of the RESs from power system using electronic devices, the RESs have no natural frequency response capability, which result in decreasing of entire power system equivalent inertia with large penetration of RESs. (ii) DoS attacks cause the insecurity of network communication, which can vanish data transmission and yield data packet dropout. To address the above issues, a virtual synchronous generator (VSG) incorporated with load frequency control (LFC) method (VSG-LFC) is proposed by introducing a hybrid-triggered mechanism (HTM) to (i) reduce the system sensitivity to the penetration level of RESs; (ii) compromise between frequency regulation performance and the reasonable usage of network resource under unsecured communication environments. Then, a switched system model is constructed considering non-identical DoS attack situations. Furthermore, theoretical analyses using Lyapunov functional related methods are carry out for asymptotical mean-square stability with H∞ performance on frequency regulation. Simulation evaluations of power system verify the effectiveness of this development. It shows that in a two-area power system frequency derivation performance indicators (IAE, ITAT, ISE) in Area1 are reduced by (82.996%, 86.273%, 85.823%) and in Area2 are reduces by (77.996%, 83.613%, 82.315%).

RELATIONSHIP BETWEEN RAINFALL VARIABILITY AND MAIZE YIELD IN APA LOCAL GOVERNMENT AREA OF BENUE STATE, NIGERIA

This study investigated the relationship between rainfall variability and maize yield in Apa Local Government Area of Benue State. The specific objectives were to analyse the trend in rainfall distribution and its impact on maize yield and to provide recommendations to improve maize productivity in the study area. Rainfall data was sourced from the archives of the Nigerian Meteorological station, Oshodi, Lagos; the maize yield data was collected from the archives of Benue State Agricultural and Rural Development Agency (BNARDA). The rainfall and maize yield data for 33 years (1988-2021) were used to characterise the maize yield response to rainfall pattern (increase or decrease). Trend-line equation was used to show the trend of rainfall, while Pearson's Correlation Coefficient (r) showed the degree of relationship between rainfall and maize yield. The results of the trend-line regressions showed an increase in total annual rainfall (y = 7.1873x + 1106.4) and an increase in maize yield (y = 0.0319x + 2.3239). The correlational trend chart showed a positive relationship between total annual rainfall and maize yield (r = 0.19), indicating that rainfall positively influenced yield. The study recommended that early warning weather information and climate-resilient varieties are needed to improve maize productivity. Extension services should also be provided to enhance farmers' knowledge on rainfall variability and its effects on maize yield.

Integrating Cover Crops into Soybean Systems in the Southern Great Plains: Impacts on Yield and Yield Components

The implementation of cover crops in crop rotation is a suggested soil health practice. As cover crops are not harvested and sold, they do not directly provide monetary gain to producers. Therefore, it is imperative that planting cover crops does not negatively affect the subsequent cash crop. However, there is no overall consensus in the literature regarding the effects of cover crops on cash crop yield. To better understand the effects of cover crops on soybean growth and yield in Oklahoma soybean systems, trials were conducted in Bixby, OK, in 2019 and Perkins, OK, in 2019, 2020, and 2021. The objectives of these trials were to (1) determine how different cover crop mixes affect soybean growth parameters and (2) determine if cover crops significantly influence soybean seed yield. Treatments within the trials included six cover crops and a fallow treatment. Soybeans were planted after the termination of the cover crops. Yield and growth parameter data were collected after harvest. No significant differences or consistent trends were detected in the yield among treatments. While cover crops had a significant impact on the number of three- and four-bean pods, no other differences in the growth parameters existed, and they never translated into significant yield differences. Based on our data, cover crops would not benefit the overall cash crop production in the continuous cover crop soybean system in Oklahoma. However, the fact that cover crops did not consistently or significantly reduce soybean yield allows growers to explore other benefits, such as weed management or soil health improvement.

MORPHO-PHYSIOLOGICAL DISSECTION OF HEAT TOLERANCE IN CERTAIN POOL OF SOYBEAN GENOTYPES

Under the scenario of climate change, crop diversification through the incorporation of non-conventional oilseed crops like soybean in the cropping pattern is a need of the time. However, being native to regions having mild temperatures, soybean faces difficulty in adaptation in areas of lower latitude with high temperatures and short day lengths during growing periods. This situation directs the researchers to determine the tolerance of soybean genotypes against high temperatures and to establish a selection criterion for the identification of adaptable traits. For this purpose, a study was conducted at Oilseeds Research Institute, Faisalabad. Forty soybean genotypes including approved varieties were screened for tolerance against high temperatures in the growth chamber and glass house. At the seedling stage, genotypes were categorized into tolerant, moderately tolerant, and susceptible categories based on their STI values in the growth chamber and values of certain morpho-physiological parameters in a glass house. Considering the seed yield an ultimate goal in the adaptation process, its study is crucial in field conditions. Tolerant and moderately tolerant genotypes from previous experiments were sown in the field. Data of yield and contributing traits were recorded at maturity and subjected to analysis. Results showed great variability among genotypes for all the traits. AARI Soybean gave the best yield followed by Faisal Soybean, ORI-SOY-91, and ORI-SOY-102. All of these genotypes/varieties also performed well in terms of electrolyte leakage and leaf-relative water content. To conclude, these varieties would show better adaptation in high-temperature areas. Moreover, both the varieties along with 2 best-performing genotypes may prove to be better parents in future hybridization programs.

Applied fixed effect of Geographically Weighted Panel Regression (GWPR) with M- Estimator approach to estimate sugarcane yield data in East Java

Applied fixed effect of Geographically Weighted Panel Regression (GWPR) with M- Estimator approach to estimate sugarcane yield data in East Java

Machine learning prediction of bio-polyol yields and hydroxyl values from acid-catalyzed liquefaction of lignocellulosic biomass

Amid the escalating demand for alternatives to petroleum resources and the imperative to decrease carbon emissions, there is an increasing interest in transforming lignocellulosic biomass into valuable chemicals. This study utilized machine learning models to analyze the acid-catalyzed liquefaction process of lignocellulosic biomass and to predict and characterize the yield and hydroxyl value (HV) of bio-polyols. A total of 612 yield data samples and 229 HV data samples were collected and analyzed. From these data, four machine learning models were constructed: Random Forest (RF), Gradient Boosting Regression (GBR), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). The Bayesian Optimization Algorithm (BOA) was applied to refine the hyperparameters of the models, thereby enhancing their predictive accuracy. To clarify the effects of input features on predictive outcomes and to understand their interactive dynamics, one-dimensional and two-dimensional Accumulated Local Effects (ALE) analyses were performed. The results show that the GBR model was particularly precise in forecasting the yield and HV of bio-polyols, with corresponding test set determination coefficients (R2) of 0.82 and 0.91. The most influential factors on yield were reaction time (15.1 %), lignin content (13.1 %), and glycerol mass (12 %), while for HV, they were glycerol mass (28.9 %), particle size (15.1 %), and liquid-to-solid ratio (11.1 %). The ALE analysis also exposed the intricate interaction mechanisms between feedstock composition and liquefaction conditions. The verification results confirmed that the optimal model displayed exceptional generalization capabilities, with a Mean Absolute Percentage Error (MAPE) of 9.18 %. Utilizing this model, a user-friendly software package has been developed to enable rapid and precise prediction of the lignocellulosic biomass conversion process. This research not only delivers strategies to cut down on experimental costs and time but also offers a novel perspective on the industrial utilization of biomass-derived polyols.

The Effect of Fertilizers and Growth Regulators on the Productivity of Safflower (Carthamus tinctorius L.) in the Dry Climate of Russia

Background: Safflower is an oilseed crop capable of forming seeds in extreme conditions, so it can be used as an alternative to sunflower, because their growing technologies are similar. Increasing the yield of safflower seeds can be achieved intensively using agrochemicals. The proposed research results provide information on the use of mineral, micro fertilizers and growth regulators. Methods: Research was carried out in the dry steppe zone of chestnut soils. In the spirit-factor experiment, which was located on a field with an area of 300 m2, in triplicate, using two sorts of safflower. Mineral fertilizers were applied to the soil during sowing and by foliar treatment during the “budding” phase. The obtained yield results were processed by analysis of variance. Result: As a result of the research, elite safflower seeds were obtained using one of the experimental options Ultamag Bor 30 g + Biostim Growth 30 g/10 liters of water of the Alexandrite variety with a yield of 1.8 t/ha with the highest profitability of their production of 414.2%. Analysis of variance of the yield data from the experiment showed that 87% of the yield of safflower seeds depended on the use of mineral fertilizers, 0.62% on the plant variety and agro technical measures for growing safflower plants-11.8%.

A Bayesian approach to assessing uncertainty in the effect of fertilization strategies on paddy rice yield via multiple on-farm experiments

ABSTRACT The surge in global agricultural input prices, particularly for nitrogen (N) fertilizers, has presented challenges for farmers facing stagnant crop prices and rising production costs. This study explored a system designed to sustain or enhance farmers’ income while reducing production costs, with a specific focus on implementing practices that involve cultivating crops with lower rates of fertilizer than conventional rates, particularly in smaller land settings, through on-farm experimentation (OFE). OFE was conducted over three years across 11 fields in Gifu, Japan, utilizing a strip trial design to investigate the effect of fertilizer input rates on rice yield. To collect yield data, unmanned aerial vehicle (UAV)-mounted multispectral cameras were deployed. A Gaussian process model was used to address the impact of fertilizer input rates on yield while accounting for spatial yield variation. The study further assessed the number of field trials achieving positive profits at certain probability levels by integrating outcomes from multiple OFEs. The results highlighted little rice yield response to fertilizer input in most of the fields, while several fields showed significant positive yield responses. Notably, the use of lower fertilizer input rates in certain fields proved to be more profitable whereas higher fertilizer input rates were less profitable in most fields. The results further emphasized the economic viability of lower rates, especially when fertilizer prices are high, thus contributing valuable insights for sustainable and profitable rice production in Japanese cultivation.

Evaluation of white seeded sesame (Sesamum indicium L.) genotypes on growth and yield performance in Menit Goldya Woreda of West Omo Zone, SWE

Sesame is the second export crop next to coffee in annual export, indicating the highest 14% of the total world export of the crop. In Africa, Sudan is the major sesame producer, followed by Nigeria, Somalia, Uganda and Ethiopia. But lack of well-adapted varieties are one of the bottleneck production problems that account for low yield and small areas cropped to the nation. Thus it is essential to evaluate such genotypes, which have high yield potential and are suitable to local environmental conditions. Therefore, field experiments were conducted at west Omo zone during 2019/20 main cropping seasons at one location of West- the Omo zone, southwest Ethiopia. The location was Menit Goldiya districts of West Omo Zone, through evaluation and selection of high-yielding sesame varieties. The study comprised of five improved and one local sesame varieties laid out in RCBD with three replications at each location. The growth and yield data were collected and analyzed by using SAS Version 9.3 statistical software. The result of the study revealed that all of the parameters considered were significantly (p < 0.01) affected by varieties on both years. Accordingly, at Menit Goldiya for, all parameters in both years and the combined analysis over the years indicated that maximum plant height, number of primary branches per plant, number of capsules per plant, thousand seed weights in grams and seed yield were observed for the Humera-1 variety. Likewise, the shortest days to flowering, early maturing, the highest length of capsule bearing zone, the maximum number of capsules per plant, the highest number of seeds per capsule and the maximum seed yield (8.13 Qu/ha) were observed for the Humera-1 variety in 2019/2020 growing seasons. Based on the mean total seed yield of locations and different yield evaluation methods, a high-yielding variety was identified. Accordingly, the Humera-1 variety performed best on both years. Therefore, this variety can play a vital role in food self-sufficiency and food security of the south-western region and it should be widely distributed to farmers of the testing locations and similar areas of the region.