Yield Trends Research Articles

Analysis of Trend, Growth, and Instability Index in Output, Harvested Area, and Yield of Oil Palm Fruit in Nigeria

The research examined the trend, growth rate, and instability index related to the output, harvested area, and yield of oil palm fruit in Nigeria. Utilizing secondary data sourced from the Food and Agricultural Organization (FAO) covering the period from 1961 to 2022, the study revealed inconsistent patterns in the trend of output, harvested area, and yield of oil palm fruit across the specified timeframes. The compound growth rates identified were 1.11% for output, 1.03% for harvested area, and 0.08% for yield. The Cuddy-Della Valle instability index (CDI) and the Coppock Instability Index (COI) were calculated at 13.87 and 46.57 for output, 13.87 and 46.02 for harvested area, and 2.52 and 37.88 for yield, respectively. These instability indices were relatively low, indicating limited activity within the oil palm fruit sub-sector in Nigeria. A decomposition analysis of the total effect of the output showed that the area effect accounted for 94.20%, the yield effect for 3.19%, and the interactive effect for 2.61%. The findings indicate that the area effect is the predominant factor driving the growth of oil palm fruit production in Nigeria. Consequently, it is recommended that various programs such as; small grower schemes, oil palm farmers’ cooperatives, and off-takers scheme be implemented within the sub-sector to stimulate increased activities, production, and yield. Also, the oil palm fruit farmers should be provided with quality seeds and financial resources in addition to broadening research efforts aimed at developing high-yield varieties.

Bacillus spp. as microbial factories for levan and fructooligosaccharide production - Recent trends.

Levan is a fructan-type homopolysaccharide that has gained increasing attention due to its unique properties and promising applications. It is a fructose-based polymer produced through microbial fermentation by diverse microorganisms, including bacteria, yeasts and archaea. The ongoing research on levan mainly focuses on optimizing production processes, elucidating its biological functions, and uncover novel applications. Studies on novel microbial producer strains and fermentation conditions have led to advancements in production processes. Remarkably, the production of levan using the native producer Bacillus spp. has shown promising trends in yield and molecular weight distribution of levan and levan-type fructooligosaccharides. Ongoing studies on levan highlight the importance of understanding the complexities of levan production for commercial scalability. The present article explores the potential of Bacillus spp. as native levan producers, presenting insights into synthesis mechanisms, factors influencing the production of levan, and genetic modifications aimed at enhancing production efficiency and altering properties.

Temporal trends in winter wheat yield: the role of NPK-fertilization and climate over decades of field experiments

Temporal trends in winter wheat yield: the role of NPK-fertilization and climate over decades of field experiments

Machine learning on multi‐spectral imagery to estimate nutrient yield of mixed‐species cover crops

AbstractThis study aimed to estimate mixed‐species cover crop (CC) biomass and nutrient contents using remote sensing, as ground‐based measurements are time‐consuming and costly. Eleven CC treatments with varying grass‐legume proportions (GLP) were sampled, and nutrient contents were determined along with multispectral imagery captured during the first and fourth weeks of March and the fourth week of April 2023. Biomass N (R2 = 0.46–0.60) and K% (R2 = 0.41—0.71) decreased with increasing GLP. The chlorophyll absorption ratio index and the normalized difference vegetation index closely followed the biomass nutrients N, P, and K combined yield (Bio_NPK) trend. Machine learning algorithms random forest (RF) and partial least square (PLS) regression were better for biomass (R2 = 0.74 with RF) and N% (R2 = 0.72 with PLS) prediction compared to the Bio_NPK prediction. These results are crucial for scientists to devise appropriate analysis approaches for estimating the benefits of mixed‐species CC.

The Determinants of Marginal Convenience Yield in Agricultural Commodity Markets

AbstractWe report a significant downward trend in the convenience yield for holding physical inventory in agricultural commodity futures markets, attributing this negative trend to speculative demand shocks, which in turn, leads in decreasing agricultural convenience yields. Moreover, agricultural convenience yields appear negative on average during the recent financialization (of commodities) period. We additionally show that the response of agricultural convenience yields to commodity price uncertainty and supply shocks is much less pronounced in magnitude and persistence compared to that of hedging demand shocks. Overall, our analysis verifies the Keynesian theory of normal backwardation by showing a long‐lasting positive response of agricultural convenience yields to a hedging demand shock, thereby leaving the hedging demand as the most significant factor explaining the less frequently observed backwardations in agricultural futures markets.

Systematic analysis of an attosecond pulse generation by a subcycle laser field

We investigated the influence of subcycle driving fields on high-order harmonic generation (HHG), with a focus on driver's intrinsic chirp, carrier-envelope phase (CEP), and number of laser cycles. Our findings reveal that the center frequency of a laser pulse scales as τ−5/4 with pulse duration τ, and that attochirp exhibits a similar dependence on pulse duration. Additionally, we identified CEP-specific trends in harmonic yield: it increases as τ5/4 for ϕ0=0∘ and decreases as τ−4.1 for ϕ0=−90∘. Although subcycle pulses can generate intense isolated attosecond pulses (IAPs), they also tend to produce higher attochirp and reduced cutoff energies. However, effective compensation for attochirp can mitigate these drawbacks, thereby increasing the capability of subcycle pulses to generate short-duration, high-intensity IAPs. These results offer valuable insights into HHG using subcycle pulses and have important implications for the advancement of ultrafast light sources and the understanding of ultrafast phenomena at the attosecond timescale. Published by the American Physical Society 2025

GIS-Based Analysis of Crop Yield Trends in Response to Climate Change Using Data-Driven Analysis Systems (DDAS) Techniques

Climate variability poses significant challenges to crop production, particularly in vulnerable regions like Southeast Nigeria, where changing environmental conditions directly impact agricultural productivity. This study employs GIS-based analysis and Data-Driven Analysis Systems (DDAS) to assess the effects of climate variables on crop yields, focusing on temperature, rainfall, relative humidity, solar radiation, and wind factors. The objectives are to (1) analyze the Relationship Between Climate Variables and Crop Yield Trends, (2) develop Predictive Models Using GIS and DDAS Techniques, and (3) identify Climate-Sensitive Zones for Targeted Agricultural Interventions within Southeast Nigeria. Findings reveal that maximum and minimum temperatures and moderate rainfall positively influence crop yield, while high relative humidity and solar radiation have a negative impact. GIS mapping and DDAS techniques successfully highlight spatial variations and provide robust yield predictions, emphasizing their value in agricultural planning. By identifying high-risk areas, such as Abakaliki, the study provides actionable insights to develop adaptive, region-specific agricultural strategies, thereby enhancing food security and sustainable agricultural practices in Southeast Nigeria.

Global Impact Assessment of Internal Climate Variability on Maize Yield Under Climate Change

AbstractInternal climate variability (ICV) is well‐known to mask forced climate change patterns and is thus expected to also impact crop yield trends. To date, a global picture of ICV effect on crop yield projection remains unclear, which inhibits effective adaptation and risk management under climate change. By combining initial condition large ensembles from multiple climate models with machine‐learning based crop model emulators, an ensemble of 2002 global maize yield simulations are conducted. The ICV effect is quantified for by the middle and end of 21st century under the business‐as‐usual scenario. ICV is shown to have significant influence on both the magnitude and sign of future yield change, with relatively higher impact in the top producing countries. The results imply that future yield projections considering relatively limited samples of ICV can be highly misleading as they may, by chance, indicate low yield loss risk in areas which will, instead, be at high risk (or vice versa). Further analysis reveals that the ICV effect is 2.30 ± 0.02 and 1.25 ± 0.03 times larger for yield projections than temperature and precipitation projections, respectively, suggesting an amplification of ICV effect from climate system to agricultural system. This study highlights that crop yield projections are substantially more uncertain than climate projections under the influence of ICV.

Revealing Climate-Induced Patterns in Crop Yields and the Water-Energy-Food-Carbon Nexus: Insights from the Pearl River Basin

In the context of growing concerns over food security and climate change, research on sustainable agricultural development increasingly emphasizes the interconnections within agricultural systems. This study developed a regionally integrated optimization and prediction agricultural model to systematically analyze the impacts of climate change on agricultural systems and their feedback mechanisms from a water-energy-food-carbon (WEFC) nexus perspective. Applied to the Pearl River Basin, the model evaluates future trends in grain yield, water use, energy consumption, and carbon emissions under various climate scenarios throughout this century. The results indicate that rising temperatures significantly reduce crop yields, particularly in the western basin, increasing the environmental footprint per unit of grain produced. However, the CO2 fertilization effect substantially offsets these negative impacts. Under the SSP585 scenario, CO2 concentrations rising from 599.77 ppm to 1135.21 ppm by the century’s end led to a shift in crop yield trends from negative (Z = −7.03) to positive (Z = 11.01). This also reduces water, energy, and carbon footprints by 12.82%, 10.62%, and 10.59%, respectively. These findings highlight the critical importance of adaptive management strategies, including precision irrigation, optimized fertilizer use, and climate-resilient practices, to ensure sustainable agricultural production. Despite these insights, the model has limitations. Future research should incorporate uncertainty analysis, diverse adaptation pathways, and advanced technologies such as machine learning and remote sensing to improve predictive accuracy and applicability. This study offers valuable guidance for mitigating the adverse impacts of climate change on the WEFC nexus, supporting sustainable agricultural practices and science-based policy development.

Wavelength Sensitive Plastic Photodissolution: Elucidating Quantum Yield Trends for Solar Activation Spectra.

Plastic photodissolution into dissolved organic carbon (DOC) is a key proposed loss pathway for plastic in aquatic environments. However, the specific solar excitation wavelengths that drive photodissolution remain unknown, limiting our ability to model and predict photodissolution rates in natural aquatic environments. To better understand the impact of solar excitation wavelength on plastic photodissolution rates, we measured the wavelength sensitivity of photodissolution for a variety of transparent and semitransparent commercial and postconsumer plastic films with wide-spanning polymer chemistries. We irradiated plastic films using custom-built light-emitting diode (LED) photoreactors that emit light in the range of 275 to 445 nm and found that plastics exhibit a strong wavelength sensitivity, producing the highest DOC release rates for short wavelength ultraviolet (UV) light. We additionally calculated photodissolution quantum yield trends for transparent plastic films and then calculated photodissolution activation spectra. We found that solar UV light between 300-350 nm is responsible for most of the plastic photodissolution for all plastic compositions and predicted plastic photodissolution rates within water columns. Results advance our ability to model plastic photodissolution in natural aquatic environments.

CT Pulmonary Angiogram Clinical Pretest Probability Tool: Impact on Emergency Department Utilization

ObjectiveCurrently, computed tomographic pulmonary angiogram (CTPA) for evaluating acute pulmonary embolism (PE) in Emergency Departments (EDs) is overused and with low yields. The goal of this study is to assess the impact of an evidence-based clinical decision support (CDS) tool, aimed at optimizing appropriate use of CTPA for evaluating PE. MethodsThe study was performed at EDs in a large healthcare system and included 9 academic and community hospitals. The primary outcome was the percent difference in utilization (number of CTPA performed/number of ED visits) and secondary outcome was yield (percentage of CTPA positive for acute PE), comparing 12 months before (6/1/2021-5/31/2022) vs. 12 months after (6/1/2022-5/31/2023) a system-wide implementation of the CDS. Univariate and multivariable analyses using logistic regression were performed to assess factors associated with diagnosis of acute PE. Statistical process control (SPC) charts were used to assess monthly trends in utilization and yield. ResultsAmong 931,677 visits to Emergency Departments, 28,101 CTPAs were performed on 24,675 patients. 14,825 CTPAs were performed among 455,038 visits (3.26%) pre-intervention; 13,276 among 476,639 visits (2.79%) post-intervention, a 14.51% relative decrease in CTPA utilization (chi-square, p<0.001). CTPA yield remained unchanged (1371/14825=9.25% pre- vs. 1184/13276=8.92% post-intervention; chi-square, p=0.34). Patients with COVID diagnosis prior to CTPA had higher probability of acute PE. SPC charts demonstrated seasonal variation in utilization (Friedman test, p=0.047). DiscussionImplementing a CDS based on validated decision rules was associated with a significant reduction in CTPA utilization. The change was immediate and sustained for 12 months post-intervention.

Rumen-protected methionine supplementation improves lactation performance and alleviates inflammation during a subclinical mastitis challenge in lactating dairy cows

This study aimed to evaluate the effects of rumen-protected Met on lactation performance, inflammation, and immune response, and liver glutathione of lactating dairy cows during a subclinical mastitis challenge (SMC). Thirty-two Holstein cows (145 ± 51 DIM) were enrolled in a randomized complete block design. At -21 d relative to the SMC, cows were assigned to dietary treatments, and data were collected before and during the SMC. Cows were blocked according to parity, DIM, and milk yield and received a basal diet (17.4% CP; Lys 7.01% MP and Met 2.14% MP) plus 100 g/d of ground corn (CON; n = 16) or a basal diet plus 100 g/d of ground corn and rumen-protected Met (SM, Smartamine M at 0.09% of dietary DM; n = 16), fed as a top-dress. At 0 d, the mammary gland's rear right quarter was infused with 100,000 cfu of Streptococcus uberis (O140J). Milk yield was recorded twice daily from 0 until 3 d relative to SMC. Milk samples were collected during each milking from 0 to 3 d relative to SMC, blood samples were collected at 0, 6, 12, 24, 48, and 72 h relative to SMC. The mTOR pathway activation was assessed in immune cells in blood and milk samples by measuring quantity and phosphorylation status of mTOR-related proteins, including AKT, S6RP, and 4EBP1. For the ratio of phosphorylated to total AKT, S6RP, and 4EBP1, blood samples were collected at 0, 12, and 24 h, and milk samples at 24 h relative to SMC. Liver biopsies were performed at -10 d and 24 h relative to SMC for measurement of glutathione. Linear mixed models with repeated measures were used to analyze the results. There was a trend for greater milk yield per milking (+ 0.8 kg) and per day (+1.7 kg) after SMC in SM cows compared with CON. The DMI was not affected by dietary treatments. Reactive oxygen metabolites were lower in SM cows than in CON. Milk somatic cell linear score was not affected by dietary treatments, and a score >4 at 24 h confirmed subclinical mastitis. The SM cows had greater milk fat percentage at 24 and 36 h post SMC, resulting in overall greater milk fat. Milk protein tended to be greater in SM cows than in CON. We observed greater liver glutathione in SM cows than in CON. Among inflammation biomarkers, ceruloplasmin was lower for SM cows compared with CON. In milk, greater phosphorylated (p) AKT:AKT and pS6RP:S6RP ratios were observed in immune cell populations from SM cows compared with CON. Blood neutrophils had a greater p4EBP1:4EBP1 ratio in SM cows compared with CON. Overall, our results show that Met supplementation during an SMC positively affected milk performance, lowered the risk of oxidative stress, and attenuated inflammation partially by increasing liver glutathione and immune cells' protein synthesis via mTOR signaling.

A decade of conservation agriculture in intensive cereal systems: Transitioning to soil resilience and stable yield trends in a climate crisis

Climate change jeopardizes the food security gains achieved in India since the Green Revolution, especially by impacting the productivity of the rice-wheat system in the Indo-Gangetic Plain, a region that serves as the ‘breadbasket’ of South Asia. In this study, we characterized the potential of long-term conservation agriculture (CA) based management practices (i.e., no-tillage, residue retention, and diversified rotations) to stabilize and enhance crop yields in Northwest India. The study consisted of six different production scenarios (Sc) namely Sc I: conventional rice-wheat system; Sc II: partial CA-puddled transplanted rice-zero till wheat-zero till mung bean (TPR-ZTWMb); Sc III: CA-based zero-till direct seeded rice-zero till wheat-zero till mung bean (ZTDSR-ZTWMb); Sc IV: CA-based zero till maize-zero till wheat-zero till mung bean (ZTM-ZTWMb); Sc V: Sc III + subsurface drip irrigation (SSDI); and Sc VI: Sc IV + SSDI. Long-term yield analysis indicated that the CA-based maize-wheat-mung bean system with SSDI (Sc VI) produced approximately 12%, 27% and 35% higher rice equivalent yield (REY), wheat yield and overall system yield, respectively, over to Sc I. Our study examines Wricke's ecovalence index (Wi2) and the sustainability yield index (SYI) to gauge long-term yield stability and sustainability. Consistently higher wheat yields with lower Wi2 and higher SYI were recorded in CA-based scenarios (Sc V: Wi2 = 0.82, SYI = 0.81; Sc VI: Wi2 = 0.85 and SYI = 0.82). CA-based scenarios also demonstrated stable REY over time. The soil physical properties were influenced by CA systems and compared to Sc I, bulk density was −5.89% in Sc V, followed by −3.62% in Sc III and −1.73% in Sc VI. Moreover, CA systems, Sc IV, Sc VI, and Sc V exhibited positive responses by +106%, +99 % and +72%, respectively for water infiltration rates. Overall, soil organic carbon was +83% and +69% with Sc VI and Sc V, respectively than in Sc I. By substantially enhancing soil health and crop productivity, as well as boosting resilience, CA emerges as a promising solution for meeting the increasing food demand in Northwest India and beyond.

Long-term fertilization strategies for improving productivity, profitability and water-use efficiency of soybean–wheat cropping systems

Abstract In order to recognize the best nutrient supply options for profitable and sustainable production systems, observations were recorded from 2001 to 2020 (20 years) in a long-term fertilizer experiment initiated in 1995–96 with soybean–wheat cropping systems (SWCSs) under irrigated conditions. The experiment comprised of seven treatments including control, organic, inorganic and their combinations. A combined use of 10 Mg farmyard manure (FYM)/ha (M) along with 120 kg N/ha provided statistically (P &lt; 0.05) similar yield and economic benefits to the M + NPK and also provided a positive yield trend (30.0 and 16.2 kg/ha/year) and net return (14.7 and 5.81 US$/ha/year) over the year in both wheat and soybean, respectively. The combined use of organic and chemical fertilizers, provided 32–41% higher production efficiency than their individual use. In contrast, long-term chemical fertilization provided a negative yield trend in both the crops with the highest reduction in sole N-fertilized plots ranged from −39 to −42 kg/ha/year. Water-use efficiency ranged from 3.20 to 12.3 kg/ha/mm in soybean–wheat rotation and increased almost 1.74–3.15 times in wheat and 1.30–1.80 times in soybean due to fertilizer application. A similar trend was observed for water-expense efficiency and remain closely associated with fertilization practice. Long-term chemical fertilizers declined the yield potential of the studied crops while their conjoint application with FYM in the winter season considered as an input efficient approach to sustain the overall productivity and profitability of SWCSs.

Analyzing the rainfed wheat yield gap in Northwest Iran

Wheat is one of the major crops in Iran, covering more than 36% of the total croplands and more than 70% of rainfed farmlands. However, the trend of crop yield in the past decades does not show promising performance and the average yield of rainfed wheat in Iran is as low as 1250 ​kg ​ha−1. Therefore, quantifying the rainfed wheat yield gap, and determining its causes and importance could help wheat self-sufficiency in Iran. Boundary line analysis (BLA) and regression tree (RT) models were employed to analyze the yield gap and find out the influencing factors. For this purpose, an on-farm survey was conducted in 210 rainfed winter wheat fields in East Azerbaijan province during the 2020–21 cropping season. Data were collected from local experts and farmers concerning crop yield and related management practices. Actual farmers’ yields were 1095 ​kg ​ha−1. BLA showed a significant yield gap ranging from 1755 to 2186 ​kg ​ha−1 (61.5–66.6% of attainable yield) and determined the attainable yield of 3072 ​kg ​ha−1. The BLA indicated that, among the quantitative independent variables, nitrogen fertilizer and planting density management were the important managerial practices responsible for the yield gaps. Additionally, the pronounced differences in crop yield across various rotation systems and varieties highlight their critical role in realizing crop attainable yield as the qualitative independent variables. By the regression tree (RT) model, the yield gap was determined about 65%. Crop rotation, crop variety, and sowing date were determined as key factors influencing the rainfed wheat yield gap. Findings showed that the combination of BLA and RT methods can be used effectively to quantify the crop yield gap and its influencing factors. Regarding the large yield gap, there is a good opportunity to obtain higher yields by optimizing managerial practices/inputs.

Combined effects of urbanization and climate variability on water and carbon balances in a rice paddy-dominated basin in southern China

Abstract Urbanization is known to elevate storm runoff, but how it influences carbon cycle and ecosystem productivity through altering the evapotranspiration (ET) process is less clear. We examined the combined effects of urbanization including change in impervious surface area (ISA) and climate variability on the water and carbon balances of the Qinhuai River Basin (QRB) over 2001–2018. QRB represents a typical rice paddy-dominated region that experienced rapid urbanization in southern China. We improved a monthly scale water supply stress index ecosystem service model by integrating local eddy flux measurements and high-resolution remote sensing data. We found a significant downward trend in both ET (−4.6 mm yr−1, p &lt; 0.05) and gross primary productivity (GPP) (−10.4 gC m−2 yr−1, p &lt; 0.05) but a significant upward trend in water yield (Q) (+28.6 mm yr−1, p &lt; 0.05). These ecosystem function changes coincided with a 96% increase in urban areas, 1.9-fold increase in ISA, and a 37% reduction in rice paddy fields. The mean annual watershed GPP decreased from 1048 gC m−2 to 998 gC m−2 while the annual Q increased from 284 mm to 669 mm from 2001 to 2018. Scenario modeling experiments suggested that the negative impacts of loss of rice paddy fields and increase in ISA on ET and GPP overwhelmed the positive impacts of climate warming. The reduction in GPP and increase in Q were largely attributed to the increases in ISA, not necessarily due to changes in land use types (e.g. urban area). The expansion of urban area, increase in ISA and reduction in leaf area index, and increase in precipitation explained the increase in Q. Our research offers insight about the interactions of carbon and water cycles through the critical ET processes under a changing climate and land surface characteristics at a watershed level. Our modeling tool and analysis provides land managers and policy makers information for designing effective ‘Urban Nature-based Solutions’ to mitigate the negative environmental effects of urbanization on carbon and water resources.

TREND OF CASSAVA YIELD/PRODCUTION IN IN KOGI EAST SENATORIAL DISTRICT, KOGI STATE, NIGERIA

This study examined the trends in cassava yield and production in the Kogi East Senatorial District of Kogi State, Nigeria, focusing on the perceptions of local farmers regarding yield changes over the past five years and the factors influencing these trends. A total of 403 respondents were surveyed, revealing that 71.3% of the farmers perceived an increase in cassava yields, while 28.7% reported stable yields. The primary factors contributing to the perceived increase included the use of improved cassava varieties (58.1%) and climate variability (41.9%). Furthermore, the statistical analysis indicated a stationary R-squared value of 0.792, suggesting a robust model fit for predicting future production trends. Forecasts indicated a steady increase in cassava production per hectare from 2024 to 2033, with projected yields rising from 17.33 tons per hectare in 2024 to 18.54 tons per hectare by 2033. Despite these positive trends, the study identified significant challenges posed by climate variability, with 76.7% of respondents recognizing a high impact on production. The findings underscore the urgent need for targeted interventions, including the promotion of climate-resilient cassava varieties, improved access to quality planting materials, and the implementation of sustainable agricultural practices. Overall, this study provides critical insights into the cassava production landscape in Kogi East and highlights the importance of collaborative efforts among stakeholders to enhance productivity and ensure food security in the region.

Integrating multi-source remote sensing and machine learning for root-zone soil moisture and yield prediction of winter oilseed rape (Brassica napus L.): A new perspective from the temperature-vegetation index feature space

Accurately assessing root-zone soil moisture is crucial for precision irrigation, as it directly influences crop yield. The Temperature-Vegetation Index (Ts-VI) Feature Space, which combines land surface temperature (Ts) and vegetation index (VI), is widely used to evaluate root-zone soil moisture in vegetated areas. However, its effectiveness in estimating crop yield remains unclear. Therefore, the objectives of this study are: (1) to collect multispectral and thermal infrared remote sensing data from a two-year (2021–2023) field experiment on winter oilseed rape (Brassica napus L.), and to optimize and evaluate the fitting methods of the dry and wet edges of the Ts-VI feature space based on the selected vegetation indices; (2) to analyze the spatiotemporal patterns of the Temperature Vegetation Dryness Index (TVDI) derived from the optimized Ts-VI feature space and estimate root-zone soil moisture (SM) and crop yield; and (3) to precisely invert the SM and yield of winter oilseed rape in the 0–60 cm root-zone using three machine learning algorithms—Support Vector Regression (SVR), Extreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR)—based on the optimized TVDI. Results indicate that, among the various fitting methods, the polynomial fitting method shows the best performance. The performance of the root-zone soil moisture prediction models across different growth stages follows the order of budding stage > seedling stage > flowering stage, and with the increase of soil depth, the performance of the model gradually deteriorates.In the yield inversion of winter oilseed rape, TVDI effectively predicts yield, with the coefficient of determination (R2) ranging from 0.430 to 0.480 and RMSE ranging from 213.399 to 267.212 kg ha−1 during the seedling stage, R2 ranging from 0.640 to 0.747 and RMSE ranging from 110.712 to 178.133 kg ha−1 during the budding stage, and R2 ranging from 0.680 to 0.773 and RMSE ranging from 83.815 to 147.301 kg ha−1 during the flowering stage. The flowering stage effectively reflects crop yield trends and allows for accurate yield prediction of winter oilseed rape up to two months in advance. A comparison of the modeling results from XGBR, SVR, and RFR shows that XGBR provides the best fit for both root-zone soil moisture and yield predictions. Compared to linear regression models, the three machine learning models significantly improve accuracy and fit, providing more precise evaluations of root-zone soil moisture and yield. In addition, through the comparison and verification of this method in other regions, it shows that the results also have certain reference value. The combination of the Ts-VI feature space and machine learning algorithms not only enables precise monitoring of root-zone soil moisture conditions but also predicts future crop yield trends, offering valuable insights for water resource management and irrigation decision-making in precision agriculture.

Forecast of Sugarcane Yield in Chongzuo, Guangxi—LSTM Model Based on Fusion of Trend Yield and Meteorological Yield

Purpose: This paper develops a high-precision yield fusion prediction model for the sugarcane industry in Chongzuo, Guangxi, based on the trend yield and meteorological yield using the long short-term memory (LSTM) model to cope with the multiple factors affecting sugarcane production. Decision support is provided to agricultural producers, policymakers, and supply chain managers so that they can plan resource allocation, market strategies, and policy directions more effectively. Methods: The paper modeled trend yield and weather yield separately to explore the complex relationship between the two in influencing sugarcane production. Trend yields were predicted using the exponential smoothing and multilayer perceptron (MLP) models, while meteorological yields were modeled using stepwise regression. The predicted yields were used again as input variables into the LSTM deep learning network to fit the nonlinear relationship between the two yields. Results: The results showed that (1) the fusion strategy of meteorological yield and MLP trend yield adopted by the model was superior to the fusion strategy of meteorological yield and exponentially smoothed trend yield, achieving a very low mean square error (MSE) of 0.011 and a goodness of fit as high as 0.979, which indicated that the model prediction was highly in agreement with the actual yield, confirming the validity of the method. (2) The prediction curve is basically consistent with the trend of actual sugarcane yield, which predicts that the sugarcane yield in Chongzuo, Guangxi, is expected to maintain a stable and small growth trend in the next eight years. (3) The fusion prediction model proposed in this study provides an accurate and practical solution for sugarcane yield prediction in Chongzuo, Guangxi, with the unique advantage of effectively analyzing and integrating the natural and socio-economic factors affecting the yield, which is of significant reference value for the prediction of sugarcane yield in the local area and even in similar ecoregions.

Mechanism study on the influence of surface properties on the synthesis of dimethyl carbonate from CO2 and methanol over ceria catalysts

The direct synthesis of dimethyl carbonate (DMC) from CO2 and methanol has attracted much attention as an environmentally benign and alternative route for conventional routes. Herein, a series of cerium oxide catalysts with various textural features and surface properties were prepared by the one-pot synthesis method for the direct DMC synthesis from CO2 and methanol, and the structure-performance relationship was investigated in detail. Characterization results revealed that both of surface acid-base properties and the oxygen vacancies contents decreased with the rising crystallinity at increasingly higher calcination temperature accompanied by an unexpectedly volcano-shaped trend of DMC yield observed on the catalysts. In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) studies indicated that the adsorption rate of methanol is slower than that of CO2 and the methanol activation state largely influences the formation of key intermediate. Although the enhanced surface acidity-basicity and oxygen vacancies brought by low-temperature calcination could facilitate the activation of CO2, the presence of excess strongly basic sites on low-crystallinity sample was detrimental to DMC synthesis due to the preferred formation of unreactive mono/polydentate carbonates as well as the further impediment of methanol activation. Moreover, with the use of 2-cyanopyridine as a dehydration reagent, the DMC synthesis was found to be both influenced by the promotion from the rapid in situ removal of water and the inhibition from the competitive adsorption of hydration products on the same active sites.