Maturity Date Research Articles

Multi-environment field trials for wheat yield, stability and breeding progress in Germany

Multi-environmental trials (MET) with temporal and spatial variance are crucial for understanding genotype-environment-management (GxExM) interactions in crops. Here, we present a MET dataset for winter wheat in Germany. The dataset encompasses MET spanning six years (2015–2020), six locations and nine crop management scenarios (consisting of combinations for three treatments, unbalanced in each location and year) comparing 228 cultivars released between 1963 and 2016, amounting to a total of 526,751 data points covering 24 traits. Beside grain yield, ten agronomic traits, four baking quality traits, plant height, heading date, maturity date and six fungal disease infection indices are included. Additionally, we provide management records, including fertilizer use, plant protection measures, irrigation, and weather data. We demonstrate how this dataset can address four agronomic questions related to GxExM interactions. Further potential applications of the dataset include empirical analyses, genomic and enviromic analyses for breeding targets, or development of decision-supporting models for agricultural management and policy decisions.

Regression study on fruit-setting days of purple eggplant fruit based on in situ VIS-NIRS and attention cycle neural network.

In the intelligent harvesting of eggplant, the lack of in situ identification technology makes it challenging to determine the maturity of purple eggplant fruit. The length of the fruit-setting date can determine when the eggplant is ready to be harvested. This study uses deep learning techniques to predict the date of fruit maturity. First, we proposed a fruit-setting days prediction method based on fruit spectroscopy and neural networks. Second, we collected the field in situ spectral data of purple eggplant fruit during 15-33 days of fruit setting using a portable spectrometer, covering 500-1000nm. A fruit-setting time regression network combining multi-scale convolution, multi-head attention mechanism, and long short-term memory recurrent neural network was constructed using the collected in situ spectral data. The model demonstrated better fitting performance than traditional machine learning models such as backpropagation neural network, random forest, support vector machine, and partial least squares regression in the regression task of fruit-setting days. After testing various spectral preprocessing methods, the best fitting effect was found on the standard normal variate-processed dataset, with R2 of 0.876 and RMSE(root mean square error) of 2.148 days. Furthermore, the feasibility of each model module was analyzed in depth through ablation experiments, confirming each component's role in improving the model's performance. The network attention weight was also analyzed, and the model has strong detail mining ability in a specific spectral interval. In summary, the combination of visible and near-infrared spectroscopy and attention cycle neural network is an effective method to predict the fruit-setting days of purple eggplant fruit. PRACTICAL APPLICATION: A prediction method of fruit-setting days based on fruit spectral characteristics and recurrent neural network regression was proposed. A novel approach to detecting and disclosing in situ surface VIS-NIRS reflectance data of eggplant fruit during ripening is presented for the first time. A set of long-term and short-term memory networks based on multi-scale convolution and multi-head attention mechanisms was constructed for spectral data fitting. Through the ablation test method and attention weight analysis, the function of each module in the network and the interpretability of feature extraction are explored.

Phenology, growth, and seed tuber yield in potato (Solanum tuberosum L) varieties as influenced by plant density at adet, northwestern Ethiopia.

The effect of planting density on producing quality seed tubers using shoot tip cuttings and conventional methods from tubers has not been studied in Ethiopia. An experiment was conducted to determine the effects of spacing on seed tuber yield and related traits of potato cultivars at Adet Agricultural Research Center in northwestern Ethiopia during the 2023 cropping season. The treatments consisted of two potato varieties (Belete and Gera) propagated by shoot tip cuttings at four inter-row spacings (30, 40, 50, and 60cm) and intra-row spacing (15, 20, 25, and 30cm). The two varieties propagated using seed tubers at 60cm inter-row and 30cm intra-row spacing. The experiment was laid out in a randomized complete block design with three replications. The results showed that the main effects of variety, inter-row spacing, intra-row spacing, propagation method, and the interactions of variety, inter-row spacing, and intra-row spacing significantly influenced the parameters considered. The earlier tuber initiation (43 days) and 50% flowering (51 days), were recorded from Gera variety propagated using shoot tip cutting. The earlier maturity date (85 days) was recorded from Belete variety propagated using shoot tip cutting. Gera variety propagated using shoot tip cutting at 40cm inter-row spacing and 25cm intra-row spacing recorded the longest plant height (85cm). Belete variety propagated using shoot tip cutting at 30cm×15cm inter- and intra-row spacing produced the highest leaf area index (5.51). The highest seed tuber number per hill (3.96), the highest average seed tuber weight (63g), and the highest seed tuber weight per hill (248g) were recorded from propagation by seed tuber (control). Gera variety propagated using shoot tip cutting at 50cm×15cm recorded the highest seed tuber yield (21.11 tons ha-1), which is 57.3% seed tuber yield increment over seed tuber propagation (control). Based on the study findings, it is recommended to propagate potatoes by shoot tip cutting at a spacing of 50cm inter-row and 15cm intra-row for seed tuber production in the study area.

Identification and evaluation of high-performing advanced germplasm of rice through morphological and breeding value analysis

Principal component analysis (PCA) and breeding value analysis (BV) were used to discover genotypes with better features for future breeding efforts. This research included a thorough analysis of the 300 exceptional rice germplasms provided by the Bangladesh Rice Research In (BRRI) and the International Rice Research Institute (IRRI). In the PCA scheme, the dendrogram generated using the UPGMA method identified Cluster II as the most favorable. This cluster comprised 125 genotypes distinguished by remarkable plant height, growth, and grain quality. Properties such as amylose content, chalkiness, and zinc concentration, which have a notable influence on crop productivity, were also taken into consideration. A biplot analysis, specifically examining principal components 1 and 2, revealed a favorable correlation between certain genotypes located in the top right quadrant and important agronomic factors like plant height, flowering, maturity dates, yield, and grain width. Genotypes 11, 31, 36, 48, 68, 78, 119, 126, 133, 195, 218, 220, 235, 241, 265, 266, 284, and 295 had favorable traits that suggested the possibility of higher rice production. The BV analysis revealed six genotypes 86, 142, 143, 239, 282, and 295 that showed promise for future breeding endeavors. Among the six, genotype 295 (IRRI 154) showed remarkable features. These studies provided a substantial contribution to the progress of rice breeding by opening up possibilities for developing rice varieties with improved yield and quality.

Near Real Time Crop Health Status Monitoring and Mapping using Sentinel Satellite Image in Menjar Shenkora District, Ethiopia

Agricultural monitoring systems must provide timely and standardized information on crop production, status, and yield, from sub-regional to national scales. Accurate monitoring and mapping of vegetation condition and health are vital for managing crops, assessing damage, and predicting yields. Crop health monitoring is one of the important items for tracking the general health status of any crop. In this regard, remote sensing and GIS play a crucial role for monitoring crop health, providing current information that traditional methods like field surveys and sampling questionnaires struggle to obtain. Effective cropland mapping techniques are essential for regular crop monitoring. This type of monitoring demands frequents continuous data with high time and space resolution. Near real-time crop monitoring uses technologies like the Sentinel-2 satellite mission, offering a consistent 5-day revisit cycle and freely accessible data. This opens new doors for delivering timely updates and monitoring parcel-based crop health and conditions in real-time. Therefore, this study used satellite images, Global Positioning System (GPS) collected data, and parcel-based socioeconomic data. GPS and socioeconomic data were employed to validate the satellite-based near real-time crop monitoring results. Vegetation Condition Index (VCI) and the Normalized Difference Vegetation Index (NDVI) were used to evaluate crop health at different stages of the growing season and to generate time series data for crop phenology respectively. NDVI time series data was used to generate crop phenology information for four main crops: Teff, wheat, onion, and sorghum. The crop type maps for these crops at the study sites were validated with an overall accuracy of 79.26% and a Kappa value of 0.737. Additionally, the results from the current research and the field-collected data were consistent in providing information about the onset, greening, maturity, and senescence dates of each crop. These findings highlight the effectiveness of the satellite-based system for real-time agricultural crop monitoring using Sentinel-2 observations across various sites and time frames. Moreover, it helps to fill the gaps of traditional crop monitoring methods with those based on satellite technology. This system is particularly valuable for early warning purpose in areas like the current study site, where conventional crop monitoring methods and inputs are limited.

Aflatoxin B1 Contamination Association with the Seed Coat Biochemical Marker Polyphenol in Peanuts Under Intermittent Drought.

Aflatoxin B1 (AFB1) contamination (AC) increases as the severity of drought stress increases in peanuts. Identifying drought-tolerant (DT) genotypes with resistance to Aspergillus flavus colonization and/or infection may aid in developing peanuts resistant to aflatoxin contamination in the semi-arid tropics. The goal of this study is to identify DT genotypes with seed coat biochemical resistance to A. flavus infestation and aflatoxin contamination. Experiments were carried out at ICRISAT Sahelian Center; fifty-five genotypes were assessed under adjacent intermittent water-stressed (WS) conditions imposed from the 60th day after sowing to the maturity date and well-watered (WW) conditions in an alpha lattice design with two factors. The yield and its components, the incidence of A. flavus colonization, aflatoxin contamination, and seed coat total polyphenol (SCTPP) were investigated. Our findings show that the water deficit reduced the pod yield, seed yield, and haulm yield by up to 19.49%, 27.24%, and 22.07%, respectively, while it increased the number of immature pods per plant (IMPN) and the aflatoxin contamination by up to 67.16% and 54.95%, respectively. The drought tolerant genotypes ICG 2106, ICG 311, ICG 4684, ICG 4543, and ICG 1415 maintained a high yield, small number of IMPN under WS and low aflatoxin content variation between WW and WS. Our findings revealed that in the drought-tolerant genotypes ICG 1415, ICG 2106, ICG 311, ICG 4684, and ICG 4543, there was a significant relationship between the aflatoxin resistance and the seed coat total polyphenol under the two water treatments (r2 = 0.80; r2 = 0.82). This suggests that these drought-tolerant genotypes kept their seed coat intact and minimized the aflatoxin contamination under an intermittent water deficit.

Genetic improvement of important agronomic traits in Chinese wheat breeding over the past 70 years

BackgroundUnderstanding the genetic improvement patterns of agronomic traits in Chinese wheat (Triticum aestivum L.) breeding is essential for devising future breeding strategies. However, a systematic analysis of the genetic improvement of important traits in Chinese wheat is lacking. This study aimed to provide insights into the improvement progress of yield-related traits in the Chinese wheat breeding process and clarify the selection pressure on important agronomic traits in different agroecological zones. Phenotypic evaluations of 481 wheat accessions including 157 Chinese landraces (CLs) and 324 modern Chinese cultivars (MCCs), were carried out in multiple locations and years.ResultsThe population structure analyses showed that all accessions could be basically divided into CLs and MCCs subpopulations. Pearson correlation analysis revealed that the negative correlation between grain number per spike and thousand-grain weight gradually decreased while thousand-grain weight, grain number per spike, and effective tiller number exhibited synergistic improvements during the modern breeding process. Phenotypic differences among MCCs released from the 1950s to the 2000s indicated that grain number per spike and grain weight-related traits increased linearly, whereas plant height and effective tiller number decreased significantly. Furthermore, since the 1950s, the heading date, flowering date, and maturity date have become earlier, while the spike length and spikelet number per spike have not changed significantly with the advancement of breeding years. The annual genetic gain analysis of agronomic traits showed that plant height had the greatest increase (‒0.96%), followed by thousand-grain weight (0.38%), while the lowest for grain number per spike (0.13%). Phenotypic difference analysis of CLs and MCCs with different geographical origins further revealed that heading date, flowering date, plant height, thousand-grain weight, grain width, and grain thickness experienced strong selection with the same trend in seven agroecological zones. Among zones, the northern winter wheat zone experienced the strongest selection pressure, and plant height and thousand-grain weight were strongly selected in all zones.ConclusionsThis study reveals that CLs and MCCs in China with obvious phenotypic differences, plant height and thousand-grain weight were strongly selected during wheat breeding, and further improvement of wheat in China will inevitably involve a continuous increase in grain number per spike.

Determination of the Effects of Aminoethoxyvinilglycine (AVG) and Gibberellic Acid (GA3) Applications on Pre-Harvest Fruit Drop Amount and Fruit Quality in Hakko Pear Cultivar

This study was conducted to determine the effects of pre-harvest Aminoethoxyvinylgylcine (AVG) and Gibberellic acid (GA3) applications on the pre-harvest fruit drop amount and fruit quality in the 'Hakko' pear (Pyrus pyrifolia) cultivar grafted on BA29 rootstock in a private orchard in the Bala district of Ankara in 2021-2022. It was conducted in an orchard belonging to a producer. 50-100-150 mg/l AVG and 25-50-75 mg/l GA3 were applied 4 weeks before the estimated harvest date. Fruits are harvested at full maturity date; Pomological properties including cumulative drop percentage, yield per tree, fruit weight, fruit width and length, fruit stem width and length, fruit firmness, fruit color, as well as chemical properties such as soluble solid content (SSC), pH and titratable acidity (TA) were evaluated. It was observed that all doses of AVG and GA3 during the harvest period reduced the cumulative drop rate compared to the control application (6.78%). In general, the yield per tree was found to be high in GA3 applications. Both AVG and GA3 applications had statistically significant effects on fruit weight, fruit width and length, fruit flesh firmness, SSC, and pH. It was observed that both AVG and GA3 applications reduced pre-harvest fruit drop and improved fruit quality characteristics compared to the control.

Genetic Analysis of Yield, Protein, and Oil Traits in Soybean Diallel Crosses for Enhanced Crop Quality and Productivity

The study was conducted at Sakha Research Station, ARC, Egypt, during three successive seasons from 2021 to 2023. A half diallel cross of fifteen F1 and F2 generations in addition to the six soybean parental genotypes namely, Japanese 13, Giza 111, Crawford, H18L54, Nc92-2231 and Linford were evaluated to determine the genetic behavior of yield and its components traits in both F1 and F2 generations, as well as seed protein and oil contents in F1. Results indicated that mean squares of General Combining Ability (GCA) and Specific Combining Ability (SCA) were significant and highly significant for all the studied characters of soybean genotypes in F1’s and F2’s, except SCA for 100-seed weight in F2’s only. The GCA/SCA ratios were less than unity in all traits, indicating that, the non-additive gene effects seemed to be responsible for the inheritance of these traits. The parental genotypes; H18L54, Nc 92-2231 and Linford seemed to be excellent combiners for days to flowering and maturity; however, Giza 111 and Japanese 13 were good combiners for seed yield. Meanwhile, Giza 111 was a good combiner for protein and oil content; also, H18L54 and Nc 92-2231 were good combiners for protein content. The crosses C7, C9, C10, C11 and C13 were earlier for maturity date, and also showed the most negative significant desirable Ŝij effects. The best (Ŝij) values in F2 were shown in the C1, C5, C8 and C15 crosses for seed yield/plant, these crosses also had the highest seed yield values in F2. The crosses; C5 and C8 showed significant to highly significant positive heterotic effects relative to the better parent for protein% and seed yield per plant. In addition three crosses; C13, C14, and C15 had highly significant positive heterotic effects relative to the better parent for oil % and seed yield per plant.

Correlation and Regression Analyses of Disease and Agronomic Traits of Ethiopian Mustard (Brassica Carinata A. Braun.) Genotypes

Ethiopian mustard (<i>Brassica carinata A. Braun</i>) is an important oilseed crop with significant potential for food and energy production. The study evaluated 36 genotypes using a 6 x 6 lattice design to analyze correlations and regression among traits, aiming to understand their relationships and identify key traits for developing high-performing varieties. The analysis of variance revealed significant variation (p < 0.001) for traits including seed yield, flowering time, maturity date, disease resistance, thousand seed weight, oil content and oil yield; indicating the potential for genetic improvement. However, traits such as downy mildew resistance, leaf spot and branching showed non-significant variation, suggesting these traits may be more influenced by environmental factors than by genetic differences among the genotypes. Pearson correlation coefficients highlighted significant relationships among traits. Days to flowering (r = 0.687) and maturity (r = 0.029) positively correlated with yield, while disease traits negatively impacted seed yield. Notably, Thousand Seed Weight (r = 0.985) strongly correlated with yield, underscoring the importance of seed size. A multiple regression model explained 99.7% of the variation in seed yield, with a highly significant intercept (1863.35, p < 0.001). Key associations were found with secondary branches (12.32), oil content (-46.79) and oil yield (2.19). This study confirms the potential for improving Ethiopian mustard yield through genetic selection of key traits. It is recommended that breeding programs focus on enhancing seed size and disease resistance while considering environmental factors to maximize yield potential.

An improved framework for mapping and assessment of dynamics in cropping pattern and crop calendar from NDVI time series across a heterogeneous agro-climatic region.

The absence of spatial and temporal cropping information in semi-arid regions poses a significant challenge in assessing the dynamics of agricultural systems at river basin scales. Satellite remote sensing provides qualitative and quantitative information to derive vegetation dynamics over extensive areas of inherent complexities due to limitations in the availability of field data and the diverse nature of agricultural cropping practices. Utilizing phenological information derived from MODIS Normalized Difference Vegetation Index (NDVI) time series data from 2003-2004 to 2021-2022, this study derives major crop types, and cropping calendar (sowing, maturity, and harvest dates) for each season and year at 250-m resolution. This study introduces an integrated function-fitting model based on the Fast Fourier Transform (FFT) and a Lagrangian 3-point derivative-based approach to extract phenological events from the NDVI time series automatically. The derived phenological information is used in a hybrid crop classification algorithm that combines a rule-based decision tree approach (using the phenological events/dates) and a random forest classifier (using the phenological metrics) to generate the classified crop map at a river basin scale for multiple seasons and years. The implemented approach successfully captured sowing and harvesting dates for every crop growing season over 19years, with an RMSE of 9days, as observed with the available field survey data from 2015 to 2021. The classification results from this hybrid approach demonstrate an overall 82% accuracy. The proposed method shows a substantial improvement in cropping area estimation with a 60% reduction in MAE and RMSE compared to the existing algorithm. From the long time series of derived seasonal crop data (19-year analysis period), the influence of monsoonal activities and shifts on the spatial and temporal dynamics of sowing time and cropping patterns are assessed for a large river basin, demonstrating the utility of this continuous crop calendar. Further, an extensive analysis of results highlights farmers' adaptive strategies in response to dry and wet years, providing foundational insights for future studies on assessing the impacts of climate change. Hence, the hybrid framework adopted in this study for deriving a continuous crop calendar holds immense relevance for parameterizing and utilizing such information for developing river basin scale hydrologic and crop growth models for water resources planning and assessment.

Pricing of timer volatility-barrier options under Heston’s stochastic volatility model

Timer options are financial instruments that enable investors to exercise their rights on a random maturity date the realized variance reaches the level of variance budget. These options provide a stable investment opportunity for investors under the unpredictable and complex financial markets, such as global financial crisis or COVID-19 pandemic, which can induce the drastic changes of the volatility for the underlying asset. Meanwhile, in the financial markets, investors who invest in standard timer options may face the problems caused by the postponement of the exercising time for too low volatility, compared to standard vanilla options. In this regard, to overcome such disadvantages, we propose timer volatility-barrier options, which are activated and expired when the volatility arrives at a relatively low barrier level, with the original properties of the standard timer options. In this paper, by making use of the method of images, we derive an analytical formulas for the timer volatility-barrier options so that the volatility process can be driven by Heston stochastic volatility model, and verify the pricing accuracy of the timer options by comparing our solutions with those obtained from Monte Carlo simulations. Finally, we conduct numerical studies on the timer volatility-barrier options to examine the pricing sensitivities with respect to the various model parameters, and implement the discussion for pricing formula of double volatility barrier type of the timer options.

Plant-Parasitic Nematode Genera Associated with Date Palm in Central Iraq

A survey of plant-parasitic nematode genera associated with roots of mature date palm trees was conducted in central Iraq to study the occurrence, population density and geographical distribution. This study in Baghdad, Babil, Diyala, Karbela, Najaf, and Wasit governorates was conducted from 2019 to 2023, during the months from October to April. A total of 150 soil and root samples were gathered from 46 fields, and plant parasitic nematodes were extracted and morphologically identified to genus level. Eleven genera of plant parasitic nematodes were detected in the declining order of frequency: Tylenchorhynchus spp. (20%) in Baghdad, Babil, Diyala, Karbala, and Najaf; Meloidogyne spp., (17.3%) in Baghdad, Babil, Diyala, Najaf, and Wasit; Paratylenchus spp. (16%) in Diyala and Najaf; Helicotylenchus spp. (12%) in Baghdad, Diyala, Najaf, and Wasit; Tylenchus spp. (12%) in Baghdad, Babil, Diyala, Najaf, and Wasit; Aphelenchoides spp. (6.7%) in Babil, Diyala, Karbala, and Wasit; Pratylenchus spp. (4%) in Baghdad, Diyala, Najaf, and Wasit; Ditylenchus spp. (3.3%) in Babil and Najaf; Hoplolaimus spp. (1.3%) in Diyala; and Heterodera spp. and Scutellonema spp. (0.7% each) in Diyala. Population density was low in Baghdad. High population density was recorded for Meloidogyne spp., (37625 J2 /1000 cm3 soil) and lowest population density occurred with Paratylenchus spp. (20 nematodes /1000 cm3 soil) in Diyala (Al-Jadidah). This appears to be the first report of occurrences of plant-parasitic nematodes of date palm in central Iraq.

Identification of Quantitative Trait Loci Associated with Plant Adaptation Traits Using Nested Association Mapping Population.

This study evaluated 290 recombinant inbred lines (RILs) of the nested association mapping (NAM) population from the UK. The population derived from 24 families, where a common parent was "Paragon," one of the UK's spring wheat cultivar standards. All genotypes were tested in two regions of Kazakhstan at the Kazakh Research Institute of Agriculture and Plant Industry (KRIAPI, Almaty region, Southeast Kazakhstan, 2019-2022 years) and Alexandr Barayev Scientific-Production Center for Grain Farming (SPCGF, Shortandy, Akmola region, Northern Kazakhstan, 2019-2022 years). The studied traits consisted of plant adaptation-related traits, including heading date (HD, days), seed maturation date (SMD, days), plant height (PH, cm), and peduncle length (PL, cm). In addition, the yield per m2 was analyzed in both regions. Based on a field evaluation of the population in northern and southeastern Kazakhstan and using 10,448 polymorphic SNP (single-nucleotide polymorphism) markers, the genome-wide association study (GWAS) allowed for detecting 74 QTLs in four studied agronomic traits (HD, SMD, PH, and PL). The literature survey suggested that 16 of the 74 QTLs identified in our study had also been detected in previous QTL mapping studies and GWASs for all studied traits. The results will be used for further studies related to the adaptation and productivity of wheat in breeding projects for higher grain productivity.

Heading and maturity date prediction using vegetation indices: A case study using bread wheat, barley and oat crops

Contemporary crop research programs involve the evaluation of numerous micro-plots spread across extensive experimental fields. As a result, there is a growing need to depart from labor-intensive manual measurements when assessing phenological data. The growing significance of high throughput phenotyping platforms (HTTP), including unmanned aerial vehicles (UAVs), has rendered these technologies essential in crop research. The overall objective of this study is to explore and validate the use of HTTP methodologies, specifically the potential of vegetation indices (VIs) derived from conventional RGB images, to forecast the date of heading (DH) and maturity (DM) for various cereal crops under different irrigation conditions. To pinpoint DH and DM prediction, a total of nine UAV surveys were conducted throughout the entire crop cycle. Prediction models for DH and DM using VIs were successfully developed for various crop species, explaining 65 % of the variance in bread wheat and 75 % in oats. The highest percentages of variance explained were achieved when models were developed separately for the two irrigation conditions (well-irrigated and rainfed). However, the percentage of variance explained by these models decreased when applied to barley (R²<0.5 for DH). Notably, including final plant height as a predictor increased the percentage of variance explained by the models only for irrigated bread wheat. Furthermore, the utilization of multi-temporal equations, which amalgamated data from diverse UAV surveys, notably enhanced the percentage of variance explained by the model (+160.71 % improvement in DH predictions), particularly those tailored to each specific crop species and irrigation condition. The investigation additionally established a thorough protocol for modeling the phenological aspects of cereal crops utilizing data acquired from UAVs, thereby enhancing the accessibility of this technology for measurements of phenology in large crop research programs.

A novel mathematical method to estimate rice phenological parameters across spatial scales for the ORYZA model

While crop models are increasingly applied to large-scale areas, inadequate observation data make it difficult to calibrate the model’s phenological parameters at the regional scale. The present study proposed a simple mathematical method for estimating rice phenological parameters across spatial scales for the ORYZA model. The method establishes the cumulative function of the phenological parameters (CFPP). By fitting the CFPP with the so-called growth curve, the 4 phenological parameters of the ORYZA model were transformed into 3 fitted parameters in the equation of CFPP. Functions between the fitted parameters and several meteorological and field management factors were established. These established functions were substituted back into CFPP to construct a modified CFPP. Due to the inter-translational relationship between CFPP and the original phenological parameters, the values of phenological parameters could be estimated by the modified CFPP based on meteorological and field management factors. The newly proposed mathematical method was applied in the Yangtze River Basin (YRB), China. The results indicated that the multi-station average of the absolute value of relative errors for the rice panicle initiation, flowering, and maturity dates within the YRB were 12.3 %, 10.5 %, and 8.7 %, respectively, which were at most 4.8 % larger than that simulated using parameters calibrated using each station’s phenological data. The phenological parameters estimated by the novel mathematical method had close performance to those calibrated directly based on observed data at most stations in terms of rice phenology simulation. The present study provided a new solution for phenological parameter calibration for crop models when applied in a large-scale area.

Improving the estimation accuracy of wheat maturity date by coupling WheatGrow with satellite images

Accurate estimation of wheat maturity date (MD) is helpful to make reasonable harvest planning and guarantee crop yield and quality. In this study, wheat phenology extracted from satellite images was assimilated into WheatGrow model to develop wheat maturity date estimation model. Theoretical uncertainty was introduced into assimilation system as the error covariance matrix of remote sensing observations, which improved the performance of maturity date estimation model. Compared with the simulated maturity date of crop growth model and assimilation system combined with the constant uncertainty (Assimilation1), the accuracy of assimilation system combined with the theoretical uncertainty (Assimilation2) was higher (r = 0.81, RMSE = 4.5 d). Assimilation2 has better performance and robustness in different years and different subregions. The mean relative errors between the estimated values of Assimilation2 and the observations were generally small and concentrated in the range of −5 % to 5 %. The estimated maturity date showed latitude variation in spatial distribution in the Huang-Huai-Hai Plain (HHHP). In addition, the trend of wheat maturity date from 2001 to 2020 in the central region of HHHP was significant (p < 0.05), and the mean change rate of maturity date reached 3–6 d/10a. However, the overall change trend of maturity date in the HHHP was not significant. Temperature was main driver affecting the spatiotemporal variation of wheat maturity date. The regional wheat maturity date estimation model can provide technical support for wheat maturity date estimation at regional scale.

Sustainable and responsible investment (SRI) Sukuk: lessons from Khazanah’s Sukuk Ihsan

Purpose SRI Sukuk, with its outcome-based emphasis, aims to align the Islamic finance industry with its original ideals and address criticisms related to form over substance. In Malaysia, while the pioneering Sukuk Ihsan was a “social” sukuk, recent SRI Sukuk issuances have predominantly been “green” or “sustainable” sukuk. This paper aims to evaluate the Malaysian SRI Sukuk market, identifying factors favouring “green” sukuk. It also examines whether structural issues in Sukuk Ihsan deterred subsequent issuers from “social” sukuk. The emergence of SRI Sukuk responds to sustainable development goals and the shift towards a low-carbon economy. Sukuk Ihsan, as the first Shariah-compliant pay-for-success structure, poses complexity and risk management challenges to meet performance criteria. Design/methodology/approach The study used a qualitative method in the form of a critical review of literature, interview sessions with experts and stakeholders who are familiar with SRI Sukuk and Sukuk Ihsan and a case study analysis of Sukuk Ihsan. Findings The popularity of “green” sukuk reflects the growing global environmental consciousness. The main factors driving the popularity of “green” sukuk are the maturity of the market and the existence of a strong supporting infrastructure for “green” issuances while the positive profiling benefits and availability of incentives for “green” issuances also contribute to a lesser extent. The recommendations include the promotion of “social” sukuk by regulators through a focus on establishing a similar supporting infrastructure for “social” sukuk as there are for SRI and standard Sukuk. In addition, issuers of “social” sukuk may want to reconsider the inclusion of key performance indicators (“KPI”) into the structure of future “social” sukuk issuances. Research limitations/implications Although all respondents considered Sukuk Ihsan to be a success, some potential areas of improvement were also noted. These include the structuring of future “social” sukuk issuances with a bigger discount to compensate for the additional risk being assumed by the investor; the need to be more careful in the KPI selection process; and one respondent even went so far as to suggest the possibility of totally removing the step-down feature of Sukuk Ihsan. Practical implications Industry implications of Sukuk Ihsan study include findings that require balancing disclosure and economics by providing additional disclosure requirements for SRI Sukuk that may pose risks without corresponding benefits for issuers. KPI selection and investor confidence should also be properly identified, as KPIs are essential for the pay-for-success model to work successfully. For sukuk holders, findings indicate that any approval for waivers during issuance can impact investor confidence negatively. Investor literacy and impact understanding should also be improved for social Sukuk success. Investors should understand the different risk exposures and evolving impact requirements vital for sustainable growth. Social implications The findings provide significant implications for social impact Sukuk issuance. They include providing a substantial case study for future social impact issuances, based on the pioneering impact of Sukuk Ihsan. Furthermore, Sukuk Ihsan’s unqualified success validates the feasibility of socially responsible sukuk. Despite its early introduction, both tranches being fully subscribed reflects robust investor interest. Stakeholders were also proud of their involvement in such an initiative, viewing it as a significant achievement in creating societal impact. Originality/value Although there have been several prior studies done on Sukuk Ihsan, the focus of those studies was on its structure and the novelty of its “step down” returns structure where investors would receive lower returns if certain key performance indicators (“KPIs”) are met by Yayasan AMIR in the execution of its Trust School Programme. Bearing in mind that the first Sukuk Ihsan has a June 2022 maturity date, and the results of its KPIs were announced in December 2021, to the best of the authors’ knowledge, this is the only documented case study that comprehensively reviews Sukuk Ihsan and identifies lessons learned and/or opportunities for improvement for the benefit of potential SRI Sukuk issuers in the future.

Accumulated thermal units method for predicting development stages, and potential seed yield of sunflower (Helianthus annuus) under Mediterranean conditions

The possibility of accurately assessing the emergence, anthesis and maturation dates of sunflower grown under Mediterranean conditions was investigated. Accumulated Thermal Units (TSUM) from sowing to emergence, from emergence to anthesis, and from anthesis to maturation were recorded in 14 experimental sunflower plantations, carried out in two sites with representative soils in the Thessaly Plain (central Greece) in the years 2012, 2013, 2014, 2015 and 2022. Various combinations of sowing date; irrigation and N-fertilization inputs were studied, and the corresponding phenological stages and seed yields were determined. It was found that the TSUM method is very effective for predicting the duration of the phenological stages of sunflower and thus the development rate and stages of this crop. Particularly accurate is the assessment of the emergence-anthesis period calculated at 817 degree days (°C-d) for base temperature (To) equal to 6 °C and optimum temperature (Topt) 28 °C, whereas for the anthesis-maturation period, the TSUM method needs to be adjusted for photoperiodism, using a photoperiod coefficient based on the daylength. In a simpler approach, the heat unit sum of 1213°C-d (To=0 °C) it can be used for the emergence-anthesis period (or 1418°C-d for the sowing-anthesis period), without much error, and an average period of 44 days for the anthesis-maturation period. Having determined the anthesis-maturation period, the potential seed yield of sunflower was successfully assessed, based on the global radiation prevailing during the grain filling period (i.e. last 37 days before capitulum ripening) for the treatments of optimum water and nitrogen applications. Knowledge of the seed yield potential of sunflower might be very important for land evaluation purposes and the introduction of this important crop in Thessaly more generally and many Mediterranean sites.

Predicting rice phenology across China by integrating crop phenology model and machine learning

This study explores the integration of crop phenology models and machine learning approaches for predicting rice phenology across China, to gain a deeper understanding of rice phenology prediction. Multiple approaches were used to predict heading and maturity dates at 337 locations across the main rice growing regions of China from 1981 to 2020, including crop phenology model, machine learning and hybrid model that integrate both approaches. Furthermore, an interpretable machine learning (IML) using SHapley Additive exPlanation (SHAP) was employed to elucidate influence of climatic and varietal factors on uncertainty in crop phenology model predictions. Overall, the hybrid model demonstrated a high accuracy in predicting rice phenology, followed by machine learning and crop phenology models. The best hybrid model, based on a serial structure and the eXtreme Gradient Boosting (XGBoost) algorithm, achieved a root mean square error (RMSE) of 4.65 and 5.72 days and coefficient of determination (R2) values of 0.93 and 0.9 for heading and maturity predictions, respectively. SHAP analysis revealed temperature to be the most influential climate variable affecting phenology predictions, particularly under extreme temperature conditions, while rainfall and solar radiation were found to be less influential. The analysis also highlighted the variable importance of climate across different phenological stages, rice cultivation patterns, and geographic regions, underscoring the notable regionality. The study proposed that a hybrid model using an IML approach would not only improve the accuracy of prediction but also offer a robust framework for leveraging data-driven in crop modeling, providing a valuable tool for refining and advancing the modeling process in rice.