Spectral enhancement of PlanetScope using Sentinal-2 images to estimate soybean yield and seed composition.

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.

Redesigning and validation of fertilizer use in maize for variable plant densities in central rift valley and Jimma in Ethiopia.

Due to low adoption and sub-optimal fertilizer use and planting density recommendation in maize, redesigning and testing these technologies are required. The study was conducted to evaluate redesigned fertilizer use of maize in two pant densities (32,443 and 53,333 plants ha-1 in Central Rift Valley (CRV); 27724 and 62,000 plants ha-1 in Jimma) on farmers' fields in contrasting agro-ecologies of Ethiopia. The on-farm study was conducted in the 2017 and 2018 cropping seasons with 3 × 2 fertilizer and plant density, factors in both regions of Ethiopia. In redesigned fertilizer use, nutrients were estimated based on the target yield. In this study, 40.8, 0.0, and 12.2 kg ha-1 N, P, and K were estimated for the redesigned fertilizer use in CRV (50% of water-limited potential yield (Yw) = 3.1 t ha-1) whereas in Jimma (50% of Yw = 7.5 t ha-1) 149.8, 9, 130.6 kg ha-1 N, P and K were estimated to produce the 50% of Yw. Linear mixed modeling was used to assess the effect of fertilizer-plant density treatments on maize yield and nutrient use efficiency. The result revealed that the average estimated maize yield for WOF, FFU, and RDFU fertilizer treatments were 2.6, 3.6, and 4.5 t ha-1 under current plant density (32,443 plants ha-1) in CRV whereas the average yields of these treatments were 3.2, 4.5 and 4.5 t ha-1 respectively when maize was grown with redesigned plant density (53,333 plants ha-1) in the same location. The average maize yield with WOF, FFU, and RDFU were 3.0, 4.6, and 4.6 t ha-1 with 27,774 plants ha-1 plant density in Jimma whereas the average maize yields over the two seasons with the same treatments were 4.3, 6.0 and 8.0 t ha-1 respectively when the crop is planted with 62,000 plants ha-1 plant density. The RDFU and redesigned plant density resulted in significantly higher yield compared to their respective control CRV but RDFU significantly increased maize yield when it was planted at redesigned (62,000 plant ha-1) in Jimma. FFU and RDFU were economically viable and redesigned plant density was also a cheaper means of improving maize productivity, especially in the Jimma region. Soil organic carbon and N were closely related to the grain yield response of maize compared to other soil factors. In conclusion, this investigation gives an insight into the importance of redesigned fertilizer use and redesigned plant density for improving maize productivity and thereby narrowing the yield gaps of the crop in high maize potential regions in Ethiopia like Jimma.

The effect of environmental variations on the production of the principal agricultural products in Colombia.

The agricultural sector of Colombia supports the national economy and food security due to the rich lands for cultivation. Although Colombia has a vast hydrological basin, climate change can impact agricultural productivity, generating economic and social adverse effects. For this, we evaluated the impact of some environmental variables on the production of the most sold crops using production, climatic, and hydrological data of the 1121 municipalities from 2007 to 2020. We modeled the production of coffee, rice, palm oil, sugarcane, and corn, adopting a Bayesian spatio-temporal model that involved a set of environmental variables: average temperature, minimum temperature, maximum temperature, evapotranspiration, precipitation, runoff, soil moisture, vapor pressure, radiation, and wind speed. We found that increases in the average temperatures can affect coffee (-0.2% per °C), rice (-3.76% per °C), and sugarcane (-0.19% per °C) production, meanwhile, these increases can boost palm oil (+2.55% per °C) and corn (+1.28% per °C) production in Colombia. This statement implies that the agricultural sector needs to substitute land use, promoting the production of palm oil and corn. Although our results did not find a significant effect of hydrological variables in any crop, suggesting that the abundance of water in Colombia might balance the impact of these variables. The increases in vapor pressure impact all the crops negatively (between -11.2% to -0.43% per kPa), except rice, evidencing that dry air conditions affect agricultural production. Colombia must manage the production location of the traditional products and implement agro-industrial technologies to avoid the climate change impact on crops.

Salt-tolerant plant growth-promoting bacteria as a versatile tool for combating salt stress in crop plants.

Soil salinization poses a great threat to global agricultural ecosystems, and finding ways to improve the soils affected by salt and maintain soil health and sustainable productivity has become a major challenge. Various physical, chemical and biological approaches are being evaluated to address this escalating environmental issue. Among them, fully utilizing salt-tolerant plant growth-promoting bacteria (PGPB) has been labeled as a potential strategy to alleviate salt stress, since they can not only adapt well to saline soil environments but also enhance soil fertility and plant development under saline conditions. In the last few years, an increasing number of salt-tolerant PGPB have been excavated from specific ecological niches, and various mechanisms mediated by such bacterial strains, including but not limited to siderophore production, nitrogen fixation, enhanced nutrient availability, and phytohormone modulation, have been intensively studied to develop microbial inoculants in agriculture. This review outlines the positive impacts and growth-promoting mechanisms of a variety of salt-tolerant PGPB and opens up new avenues to commercialize cultivable microbes and reduce the detrimental impacts of salt stress on plant growth. Furthermore, considering the practical limitations of salt-tolerant PGPB in the implementation and potential integration of advanced biological techniques in salt-tolerant PGPB to enhance their effectiveness in promoting sustainable agriculture under salt stress are also accentuated.

The positive impact of honeybee activity on fennel crop production and sustainability.

This study investigates the ecological interaction between honeybees (Apis mellifera) and fennel (Foeniculum vulgare) plants, examining the mutual benefits of this relationship. Field experiments conducted in Egypt from December 2022 to May 2023 recorded diverse insect pollinators attracted to fennel flowers, especially honeybees. Assessing honeybee colonies near fennel fields showed improvements in sealed brood (357.5-772.5 cells), unsealed brood (176.3-343.8 cells), pollen collection (53.25-257.5 units), honey accumulation (257.5-877.5 units), and colony strength (7.75-10) over three weeks. Fennel exposure explained 88-99% of variability in foraging metrics. Comparing open versus self-pollinated fennel revealed enhanced attributes with bee pollination, including higher flower age (25.67 vs 19.67 days), more seeds per umbel (121.3 vs 95.33), bigger seeds (6.533 vs 4.400 mm), heavier seeds (0.510 vs 0.237 g/100 seeds), and increased fruit weight per umbel (0.619 vs 0.226 g). Natural variation in seed color and shape also occurred. The outcomes demonstrate the integral role of honeybees in fennel agroecosystems through efficient pollination services that improve crop productivity and quality. Fennel provides abundant nutritional resources that bolster honeybee colony health. This research elucidates the symbiotic bee-fennel relationship, underscoring mutualistic benefits and the importance of ecological conservation for sustainable agriculture.

Identifying critical growth stage and resilient genotypes in cowpea under drought stress contributes to enhancing crop tolerance for improvement and adaptation in Cameroon.

Drought stress following climate change is likely a scenario that will have to face crop growers in tropical regions. In mitigating this constraint, the best option should be the selection and use of resilient varieties that can withstand drought threats. Therefore, a pot experiment was conducted under greenhouse conditions at the Research and Teaching Farm of the Faculty of Agronomy and Agricultural Sciences of the University of Dschang. The objectives are to identify sensitive growth stage, to identify drought-tolerant genotypes with the help of yield-based selection indices and to identify suitable selection indices that are associated with yield under non-stress and stress circumstances. Eighty-eight cowpea genotypes from the sahelian and western regions of Cameroon were subjected to drought stress at vegetative (VDS) and flowering (FDS) stages by withholding water for 28 days, using a split plot design with two factors and three replications. Seed yields under stress (Ys) and non-stress (Yp) conditions were recorded. Fifteen drought indices were calculated for the two drought stress levels against the yield from non-stress plants. Drought Intensity Index (DII) under VDS and FDS were 0.71 and 0.84 respectively, indicating severe drought stress for both stages. However, flowering stage was significantly more sensitive to drought stress compared to vegetative stage. Based on PCA and correlation analysis, Stress Tolerance Index (STI), Relative Efficiency Index (REI), Geometric Mean Productivity (GMP), Mean Productivity (MP), Yield Index (YI) and Harmonic Mean (HM) correlated strongly with yield under stress and non-stress conditions and are therefore suitable to discriminate high-yielding and tolerant genotypes under both stress and non-stress conditions. Either under VDS and FDS, CP-016 exhibited an outstanding performance under drought stress and was revealed as the most drought tolerant genotype as shown by ranking, PCA and cluster analysis. Taking into account all indices, the top five genotypes namely CP-016, CP-021, MTA-22, CP-056 and CP-060 were identified as the most drought-tolerant genotypes under VDS. For stress activated at flowering stage (FDS), CP-016, CP-056, CP-021, CP-028 and MTA-22 were the top five most drought-tolerant genotypes. Several genotypes with insignificant Ys and irrelevant rank among which CP-037, NDT-001, CP-036, CP-034, NDT-002, CP-031, NDT-011 were identified as highly drought sensitive with low yield stability. This study identified the most sensitive stage and drought tolerant genotypes that are proposed for genetic improvement of cowpea.

Deep learning-based prediction of plant height and crown area of vegetable crops using LiDAR point cloud.

Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.

Belowground plant allocation regulates rice methane emissions from degraded peat soils.

Carbon-rich peat soils have been drained and used extensively for agriculture throughout human history, leading to significant losses of their soil carbon. One solution for rewetting degraded peat is wet crop cultivation. Crops such as rice, which can grow in water-saturated conditions, could enable agricultural production to be maintained whilst reducing CO2 and N2O emissions from peat. However, wet rice cultivation can release considerable methane (CH4). Water table and soil management strategies may enhance rice yield and minimize CH4 emissions, but they also influence plant biomass allocation strategies. It remains unclear how water and soil management influences rice allocation strategies and how changing plant allocation and associated traits, particularly belowground, influence CH4-related processes. We examined belowground biomass (BGB), aboveground biomass (AGB), belowground:aboveground ratio (BGB:ABG), and a range of root traits (root length, root diameter, root volume, root area, and specific root length) under different soil and water treatments; and evaluated plant trait linkages to CH4. Rice (Oryza sativa L.) was grown for six months in field mesocosms under high (saturated) or low water table treatments, and in either degraded peat soil or degraded peat covered with mineral soil. We found that BGB and BGB:AGB were lowest in water saturated conditions where mineral soil had been added to the peat, and highest in low-water table peat soils. Furthermore, CH4 and BGB were positively related, with BGB explaining 60% of the variation in CH4 but only under low water table conditions. Our results suggest that a mix of low water table and mineral soil addition could minimize belowground plant allocation in rice, which could further lower CH4 likely because root-derived carbon is a key substrate for methanogenesis. Minimizing root allocation, in conjunction with water and soil management, could be explored as a strategy for lowering CH4 emissions from wet rice cultivation in degraded peatlands.

Novel Bacillus and Prestia isolates from Dwarf century plant enhance crop yield and salinity tolerance.

Soil salinity is a major environmental stressor impacting global food production. Staple crops like wheat experience significant yield losses in saline environments. Bioprospecting for beneficial microbes associated with stress-resistant plants offers a promising strategy for sustainable agriculture. We isolated two novel endophytic bacteria, Bacillus cereus (ADJ1) and Priestia aryabhattai (ADJ6), from Agave desmettiana Jacobi. Both strains displayed potent plant growth-promoting (PGP) traits, such as producing high amounts of indole-3-acetic acid (9.46, 10.00 µgml-1), ammonia (64.67, 108.97 µmol ml-1), zinc solubilization (Index of 3.33, 4.22, respectively), ACC deaminase production and biofilm formation. ADJ6 additionally showed inorganic phosphate solubilization (PSI of 2.77), atmospheric nitrogen fixation, and hydrogen cyanide production. Wheat seeds primed with these endophytes exhibited enhanced germination, improved growth profiles, and significantly increased yields in field trials. Notably, both ADJ1 and ADJ6 tolerated high salinity (up to 1.03 M) and significantly improved wheat germination and seedling growth under saline stress, acting both independently and synergistically. This study reveals promising stress-tolerance traits within endophytic bacteria from A. desmettiana. Exploiting such under-explored plant microbiomes offers a sustainable approach to developing salt-tolerant crops, mitigating the impact of climate change-induced salinization on global food security.

Assessing the impact of climate variability on maize yields in the different regions of Ghana-A machine learning perspective.

Climate variability has become one of the most pressing issues of our time, affecting various aspects of the environment, including the agriculture sector. This study examines the impact of climate variability on Ghana's maize yield for all agro-ecological zones and administrative regions in Ghana using annual data from 1992 to 2019. The study also employs the stacking ensemble learning model (SELM) in predicting the maize yield in the different regions taking random forest (RF), support vector machine (SVM), gradient boosting (GB), decision tree (DT), and linear regression (LR) as base models. The findings of the study reveal that maize production in the regions of Ghana is inconsistent, with some regions having high variability. All the climate variables considered have positive impact on maize yield, with a lesser variability of temperature in the Guinea savanna zones and a higher temperature variability in the Volta Region. Carbon dioxide (CO2) also plays a significant role in predicting maize yield across all regions of Ghana. Among the machine learning models utilized, the stacking ensemble model consistently performed better in many regions such as in the Western, Upper East, Upper West, and Greater Accra regions. These findings are important in understanding the impact of climate variability on the yield of maize in Ghana, highlighting regional disparities in maize yield in the country, and highlighting the need for advanced techniques for forecasting, which are important for further investigation and interventions for agricultural planning and decision-making on food security in Ghana.

An optimized crop-livestock system can achieve a safe and just planetary boundary for phosphorus at the sub-basin level in China.

The contribution of crop and livestock production to the exceedance of the planetary boundary for phosphorus (P) in China is still unclear, despite the country's well-known issues with P fertilizer overuse and P-related water pollution. Using coupled models at sub-basin scales we estimate that livestock production increased the consumption of P fertilizer fivefold and exacerbated P losses twofold from 1980 to 2017. At present, China's crop-livestock system is responsible for exceeding what is considered a 'just' threshold for fertilizer P use by 30% (ranging from 17% to 68%) and a 'safe' water quality threshold by 45% (ranging from 31% to 74%) in 25 sub-basins in China. Improving the crop-livestock system will keep all sub-basins within safe water quality and just multigenerational limits for P in 2050.

Pearl millet a promising fodder crop for changing climate: a review.

The agricultural sector faces colossal challenges amid environmental changes and a burgeoning human population. In this context, crops must adapt to evolving climatic conditions while meeting increasing production demands. The dairy industry is anticipated to hold the highest value in the agriculture sector in future. The rise in the livestock population is expected to result in an increased demand for fodder feed. Consequently, it is crucial to seek alternative options, as crops demand fewer resources and are resilient to climate change. Pearl millet offers an apposite key to these bottlenecks, as it is a promising climate resilience crop with significantly low energy, water and carbon footprints compared to other crops. Numerous studies have explored its potential as a fodder crop, revealing promising performance. Despite its capabilities, pearl millet has often been overlooked. To date, few efforts have been made to document molecular aspects of fodder-related traits. However, several QTLs and candidate genes related to forage quality have been identified in other fodder crops, which can be harnessed to enhance the forage quality of pearl millet. Lately, excellent genomic resources have been developed in pearl millet allowing deployment of cutting-edge genomics-assisted breeding for achieving a higher rate of genetic gains. This review would facilitate a deeper understanding of various aspects of fodder pearl millet in retrospect along with the future challenges and their solution. This knowledge may pave the way for designing efficient breeding strategies in pearl millet thereby supporting sustainable agriculture and livestock production in a changing world.

Influences of crop diversification on yield, resource use efficiency, and environmental footprint in farmland landscapes in intensive farming.

Enhancing crop diversification in intensive fields has the potential to increase crop yield and reduce environmental footprint. However, these relationships at the landscape scale remained unclear in intensive farming. Addressing this gap, this paper aims to elucidate how crop yield, resources use efficiency (RUE), and environmental footprint (EF) vary with crop diversification levels in the North China Plain. Management practices, including crop pattern, field size, and agronomic inputs, were collected for 421 landscapes of 1 × 1 km subplots using Sentinel-2 and Landsat-8 images and survey. The results showed that, at the landscape scale, energy and fertilizer contributed over 53 %, and 37 % of the carbon footprint, respectively. N fertilizer constituted >98 % of the nitrogen footprint. P fertilizer accounted for over 80 %, while electricity comprised >13 % of the phosphorus footprint. Compared with simplified landscapes, diversified landscapes exhibited several significant features: 1) 56 % reduction of the area ratio of winter wheat-summer maize double crop pattern (WM), 2) a significant decrease in field size, 3) the decreased use of total NPK fertilizers at 32 %, 30 %, and 30 %, respectively, 4) the increased inputs of irrigation water, diesel, electricity, pesticide and labour at 21 %, 19 %, 21 %, 77 %, and 92 %, respectively. Although yield could be reduced at 33 % when transforming simplified landscapes into moderately diversified ones, they increased with the further promotion of crop diversification. Thus, the diversified landscapes could achieve a balance in yield, RUE, and EF to enhance sustainability, whereas simplified landscapes can similarly achieve a balance to benefit productivity. We emphasize the viable potential of diversified landscapes to enhance sustainable agricultural development by optimizing crop composition. This analysis offers pioneering evidence of landscape-scale agronomic and environmental performances of crop diversification.

Accumulation and translocation of lead in vegetables through intensive use of organic manure and mineral fertilizers with wastewater.

In many countries with wastewater irrigation and intensive use of fertilizers (minerals and organics), heavy metal deposition by crops is regarded as a major environmental concern. A study was conducted to determine the impact of mineral fertilizers, cow manure, poultry manure, leaf litter, and sugarcane bagasse on soil's trace Pb content and edible parts of vegetables. It also evaluated the risk of lead (Pb) contamination in water, soil, and food crops. Six vegetables (Daucus carota, Brassica oleracea, Pisum sativum, Solanum tuberosum, Raphanus sativus, and Spinacia oleracea) were grown in the field under twelve treatments with different nutrient and water inputs. The lead concentrations in soil, vegetables for all treatments and water samples ranged from 1.038-10.478, 0.09346-9.0639 mg/kg and 0.036-0.26448 mg/L, The concentration of lead in soil treated with wastewater in treatment (T6) and vegetable samples was significantly higher, exceeding the WHO's permitted limit. Mineral and organic fertilizers combined with wastewater treatment reduced lead (Pb) concentrations in vegetables compared to wastewater application without organic fertilizers. Health risk indexes for all treatments except wastewater treatment (T6) were less than one. Pb concentrations in mineral fertilizers, cow manure, poultry manure, leaf litter, and sugarcane bagasse treated were determined to pose no possible risk to consumers.

Effects of different observed datasets on the calibration of crop model parameters with GLUE: A case study using the CROPGRO-Soybean phenological model.

Suitable combinations of observed datasets for estimating crop model parameters can reduce the computational cost while ensuring accuracy. This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. Different combinations of four observed phenological stages, including initial flowering, initial pod, initial grain, and initial maturity stages for five soybean cultivars from Exp. 1 and Exp. 3 described in Table 2 are respectively used to calibrate the CSPs. The CSPM, driven by the optimized CSPs, is then evaluated against two independent phenological datasets from Exp. 2 and Exp. 4 described in Table 2. Root means square error (RMSE) (mean absolute error (MAE), coefficient of determination (R2), and Nash Sutcliffe model efficiency (NSE)) are 15.50 (14.63, 0.96, 0.42), 4.76 (3.92, 0.97, 0.95), 4.69 (3.72, 0.98, 0.95), 3.91 (3.40, 0.99, 0.96) and 12.54 (11.67, 0.95, 0.60), 5.07 (4.61, 0.98, 0.93), 4.97 (4.28, 0.97, 0.94), 4.58 (4.02, 0.98, 0.95) for using one, two, three, and four observed phenological stages in the CSPs estimation. The evaluation results suggest that RMSE and MAE decrease, and R2 and NSE increase with the increase in the number of observed phenological stages used for parameter calibration. However, there is no significant reduction in the RMSEs (MAEs, NSEs) using two, three, and four observed stages. Relatively reliable optimized CSPs for CSMP are obtained by using at least two observed phenological stages balancing calibration effect and computational cost. These findings provide new insight into parameter estimation of crop models.

[Estimation of soil moisture and organic matter content in saline alkali farmland by using CARS algorithm combined with covariates]. / 利用CARS算法联合协变量估算盐碱农田土壤水分和有机质含量.

Rapid acquisition of the data of soil moisture content (SMC) and soil organic matter (SOM) content is crucial for the improvement and utilization of saline alkali farmland soil. Based on field measurements of hyperspectral reflectance and soil properties of farmland soil in the Hetao Plain, we used a competitive adaptive reweighted sampling algorithm (CARS) to screen sensitive bands after transforming the original spectral reflectance (Ref) into a standard normal variable (SNV). Strategies Ⅰ, Ⅱ, and Ⅲ were used to model the input variables of Ref, Ref SNV, Ref-SNV+ soil covariate (SC), and digital elevation model (DEM). We constructed SMC and SOM estimation models based on random forest (RF) and light gradient boosting machine (LightGBM), and then verified and compared the accuracy of the models. The results showed that after CARS screening, the sensitive bands of SMC and SOM were compressed to below 3.3% of the entire band, which effectively optimized band selection and reduced redundant spectral information. Compared with the LightGBM model, the RF model had higher accuracy in SMC and SOM estimation, and the input variable strategy Ⅲ was better than Ⅱ and Ⅰ. The introduction of auxiliary variables effectively improved the estimation ability of the model. Based on comprehensive analysis, the coefficient of determination (Rp2), root mean square error (RMSE), and relative analysis error (RPD) of the SMC estimation model validation based on strategy Ⅲ-RF were 0.63, 3.16, and 2.01, respectively. The SOM estimation models based on strategy Ⅲ-RF had Rp2, RMSE, and RPD of 0.93, 1.15, and 3.52, respectively. The strategy Ⅲ-RF model was an effective method for estimating SMC and SOM. Our results could provide a new method for the rapid estimation of soil moisture and organic matter content in saline alkali farmland.

A phenomenological inquiry into farmers' experiences growing cotton in Punjab, Pakistan.

Sustainability in cotton production is inevitable because producing more cotton means more employment, economic acceleration, and industrial expansion. India, China, the United States, Brazil, and Pakistan contribute 74% of worldwide cotton production. Pakistan is contributing only 5%, despite the high potential of cotton. The average yield of cotton in Pakistan is stagnant at 570.99 kg hm-2, whereas it entails the highest cost of production among all other crops. The yield obtained in Pakistan is less than the potential, profitability is drastically lessening, and farmers are abandoning cotton for alternative kharif crops. Some traditional quantitative studies have unveiled different factors that affect cotton production. However, an in-depth qualitative study has never been conducted in Pakistan to explore the root causes of growing cotton crop failure. Following Moustakas's traditional phenomenological guidelines, this phenomenological study was conducted in the district of Rahim Yar Khan in the core cotton zone of Punjab province. A total of 10 interviews were conducted with purposively selected cotton growers based on a criterion: (i) having more than 10 years of cotton growing experience, (ii) being a cotton grower, and (iii) having at least 10 years of formal schooling. Interviews were conducted face to face on an interview guide. One interview lasted 45-50 min, and responses were recorded and analyzed using a thematic analysis approach. A total of 6 themes emerged from the collected data, including (i) climate change, (ii) varietal problems, (iii) pesticide usage, (iv) sense of institutional services, (v) attitude of farmers and (vi) soil health and environment. These six merging themes contributed to cotton crop failure and yield decline. The deep exploration further summarized that researchers, extensionists, and farmers need to seriously consider variety, sowing time, and the environment to revive cotton crops. The detailed recommendations and policy guidelines are presented in this paper, highlighting the cotton sector's research, development and investment areas.

Effects of wind speed and wind direction on crop yield forecasting using dynamic time warping and an ensembled learning model.

The cultivation of cashew crops carries numerous economic advantages, and countries worldwide that produce this crop face a high demand. The effects of wind speed and wind direction on crop yield prediction using proficient deep learning algorithms are less emphasized or researched. We propose a combination of advanced deep learning techniques, specifically focusing on long short-term memory (LSTM) and random forest models. We intend to enhance this ensemble model using dynamic time warping (DTW) to assess the spatiotemporal data (wind speed and wind direction) similarities within Jaman North, Jaman South, and Wenchi with their respective production yield. In the Bono region of Ghana, these three areas are crucial for cashew production. The LSTM-DTW-RF model with wind speed and wind direction achieved an R2 score of 0.847 and the LSTM-RF model without these two key features R2 score of (0.74). Both models were evaluated using the augmented Dickey-Fuller (ADF) test, which is commonly used in time series analysis to assess stationarity, where the LSTM-DTW-RF achieved a 90% level of confidence, while LSTM-RF attained an 87.99% level. Among the three municipalities, Jaman South had the highest evaluation scores for the model, with an RMSE of 0.883, an R2 of 0.835, and an MBE of 0.212 when comparing actual and predicted values for Wenchi. In terms of the annual average wind direction, Jaman North recorded (270.5 SW°), Jaman South recorded (274.8 SW°), and Wenchi recorded (272.6 SW°). The DTW similarity distance for the annual average wind speed across these regions fell within specific ranges: Jaman North (±25.72), Jaman South (±25.89), and Wenchi (±26.04). Following the DTW similarity evaluation, Jaman North demonstrated superior performance in wind speed, while Wenchi excelled in wind direction. This underscores the potential efficiency of DTW when incorporated into the analysis of environmental factors affecting crop yields, given its invariant nature. The results obtained can guide further exploration of DTW variations in combination with other machine learning models to predict higher cashew yields. Additionally, these findings emphasize the significance of wind speed and direction in vertical farming, contributing to informed decisions for sustainable agricultural growth and development.