Crop Yield Productivity Research Articles

Future Water Requirements and Crop Productivity at Al-Najaf Governorate Under Different Climate Change Scenarios (2020–2080)

This study aims to predict the effect of climate change on net irrigation water requirements (NIWR) and agricultural productivity from five common crops (wheat, barley, summer maize, and sorghum) in the Al-Najaf Governorate in Iraq. GFDL-ESM2M mode was used to predict the lower and upper temp and precipitation for two time periods (2020-2080) with 30 years for two periods (P1 and P2) under representative concentrations paths (RCP 2.5, RCP6, and RCP8.5). The CROPWAT model is used to determine NIWR, and the extreme learning model was used to estimate agricultural yields using previous crop yield production and weather data, supported vectors machine (SVM) is executed as a Machines Learns algorithm. Results showed NIWR increment to consider cropping owing to climate change. Barley is the crop most affected by climate change under the (RCP2.5, RCP6, and RCP8.5) scenarios, with increasing crop water requirements (NIWR) of (22%, 23%, and 24% ) for P1 and (23%, 24%, and 29%) for P2, respectively. Summer maize is the crop least affected by climate change under all climate change scenarios, with increasing crop water requirements of (1%, 2%, and 4%) for P1 and (1%, 2%, and 5% for P2. Climate change negatively affects the crop yield of all crops under the different climate change scenarios. The findings of this study could be used as a guide to developing adaptation strategies for dealing with potential changes in water availability and agricultural water productivity due to climate change.

Freshwater Shortage, Salinity Increase, and Global Food Production: A Need for Sustainable Irrigation Water Desalination—A Scoping Review

Climate-change-induced freshwater shortage and saline intrusion have been posing significant risks to agricultural sectors in arid and semi-arid regions, negatively impacting irrigation, crop yield, and food production. Climate-smart sustainable solutions are the requirement to combat these major concerns. To overcome freshwater scarcity, pressure-driven desalination techniques are used that require advanced operational systems and electricity, which creates an additional economic burden when applied in the agriculture sector. Therefore, more sustainable methods for soil and water desalination using plant-, microbial-, algal-, biomass-, and carbon-based systems are needed. This scoping review addresses the effects of climate change on freshwater shortage and global food production, the influence of salinity and sodicity on agriculture, and sustainable desalination technologies.

Humic Acid-Amended Formulation Improves Shelf-Life of Plant Growth-Promoting Rhizobacteria (PGPR) Under Laboratory Conditions

Plant growth-promoting rhizobacteria (PGPR) is a soil bacterium that positively impacts soil and crops. These microbes invade plant roots, promote plant growth, and improve crop yield production. Bacillus subtilis is a type of PGPR with a short shelf-life due to its structural and cellular components, with a non-producing resistance structure (spores). Therefore, optimum formulations must be developed to prolong the bacterial shelf-life by adding humic acid (HA) as an amendment that could benefit the microbes by providing shelter and carbon sources for bacteria. Thus, a study was undertaken to develop a biofertilizer formulation from locally isolated PGPR, using HA as an amendment. Four doses of HA (0, 0.01, 0.05, and 0.1%) were added to tryptic soy broth (TSB) media and inoculated with B. subtilis (UPMB10), Bacillus tequilensis (UPMRB9) and the combination of both strains. The shelf-life was recorded, and viable cells count and optical density were used to determine the bacterial population and growth trend at monthly intervals and endospores detection using the malachite green staining method. After 12 months of incubation, TSB amended with 0.1% HA recorded the highest bacterial population significantly with inoculation of UPMRB9, followed by mixed strains and UPMB10 at 1.8x107 CFUmL-1, 2.8x107 CFUmL-1and 8.9x106 CFUmL-1, respectively. Results showed that a higher concentration of HA has successfully prolonged the bacterial shelf-life with minimal cell loss. Thus, this study has shown that the optimum concentration of humic acid can extend the bacterial shelf-life and improve the quality of a biofertilizer.

Agrivoltaic, a Synergistic Co-Location of Agricultural and Energy Production in Perpetual Mutation: A Comprehensive Review

Agrivoltaic systems, which consist of the combination of energy production by means of photovoltaic systems and agricultural production in the same area, have emerged as a promising solution to the constraints related to the reduction in cultivated areas due to solar panels used in agricultural production systems. They also enable optimization of land use and reduction in conflicts over land access, in order to meet the increasing demand for agricultural products and energy resulting from rapid population growth. However, the selected installation configurations, such as elevation, spacing, tilt, and choice of panel technology used, can have a negative impact on agricultural and/or energy production. Thus, this paper addresses the need for a review that provides a clear explanation of agrivoltaics, including the factors that impact agricultural and energy production in agrivoltaic systems, types of panel configurations and technologies to optimize these systems, and a synthesis of modelling studies which have already been conducted in this area. Several studies have been carried out in this field to find the appropriate mounting height and spacing of the solar panels that optimize crop yields, as this later can be reduced by the shade created with the solar panels on the plants. It was reported that yields have been reduced by 62% to 3% for more than 80% of the tested crops. To this end, an optimization model can be developed to determine the optimal elevation, spacing, and tilt angle of the solar panels. This model would take into account factors that influence crop growth and yield, as well as factors that affect the performance of the photovoltaic system, with the goal of maximizing both crop yield and energy production.

Soil textures and nutrients estimation using remote sensing data in north india - Punjab region

Estimating soil and plant nutrients are essential for crop growth and yield production. Quantifying nutrients by conventional methods is a time-consuming and hectic process. The hyperspectral imagery and multispectral sensors mounted on the UAV (Unmanned Aerial Vehicle) are costlier. This study used open-source remote sensing imagery and obtained ground truth data from the Punjab region to estimate nine nutrients and soil textures: Nitrogen (N), Phosphorous (P), Carbon (C), Ammonium (N-NH4), Nitrate Nitrogen (N-NO3), Calcium (Ca), Magnesium (Mg), Clay and Silt. To remove the noise from the dataset, this study applied DOS1 atmospheric correction and compared it with the raw dataset with four ensemble machine learning methods: Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Random Forest Regressing (RFR), and Adaptive Boosting (ADA) for estimation of soil texture/nutrients content. The study has three main purposes; firstly, it focuses on the less studied agriculture-intensive Punjab region. Secondly, it analyses the impact of preprocessed vs raw multispectral images. Finally, it explores the correlation of multispectral images and different soil parameters via different machine learning techniques. Results of this study suggest that the raw dataset is in general significantly better than the DOS1 atmospheric correction. Also, some parameters have better correlation with the image data compared to others. This potential approach can estimate the soil texture/nutrient content in the same region.

Applications of CNN in leaf diseases: A critical survey

Crop diseases can significantly impact crop yield and overall productivity, posing challenges for farmers in increasing output and market prices. Early detection of these diseases is crucial for preventing further spread and reducing their impact. To overcome this, researchers have utilized image processing technology, including deep learning techniques such as convolutional neural networks (CNNs), to detect crop diseases. In this critical survey, we provide a comprehensive review of recent studies and developments in the use of CNNs for identifying leaf diseases in agricultural plants. We discuss the benefits and drawbacks of different deep learning techniques and image processing methods for disease diagnosis and management in agriculture. Our research highlights the potential of CNNs and deep learning to significantly advance the field of agricultural research and development. We also analyze the factors affecting the outcomes of each technique, including the accuracy, precision. Our study emphasizes the need for further research and development to optimize the use of CNNs in agricultural applications, particularly for improving disease management and crop productivity.

An intelligent approach to improve date palm crop yield and water productivity under different irrigation and climate scenarios

Drought, rising demand for water, declining water resources, and mismanagement have put society at serious risk. Therefore, it is essential to provide appropriate solutions to increase water productivity (WP). As an element of research, this study presents a hybrid machine learning approach and investigates its potential for estimating date palm crop yield and WP under different levels of subsurface drip irrigation (SDI). The amount of applied water in the SDI system was compared at three levels of 125% (T1), 100% (T2), and 75% (T3) of water requirement. The proposed ACVO-ANFIS approach is composed of an anti-coronavirus optimization algorithm (ACVO) and an adaptive neuro-fuzzy inference system (ANFIS). Since the effect of irrigation factors, climate, and crop characteristics are not equal in estimating the WP and yield, the importance of these factors should be measured in the estimation phase. To fulfill this aim, ACVO-ANFIS employed eight different feature combination models based on irrigation factors, climate, and crop characteristics. The proposed approach was evaluated on a benchmark dataset that contains information about the groves of Behbahan agricultural research station located in southeast Khuzestan, Iran. The results explained that the treatment T3 advanced data palm crop yield by 3.91 and 1.31%, and WP by 35.50 and 20.40 kg/m3, corresponding to T1 and T2 treatments, respectively. The amount of applied water in treatment T3 was 7528.80 m3/ha, which suggests a decrease of 5019.20 and 2509.6 m3/ha of applied water compared to the T1 and T2 treatments. The modeling results of the ACVO-ANFIS approach using a model with factors of crop variety, irrigation (75% water requirement of SDI system), and effective rainfall achieved RMSE = 0.005, δ = 0.603, and AICC = 183.25. The results confirmed that the ACVO-ANFIS outperformed its counterparts in terms of performance criteria.

Correlation Analysis between Crop Yield and Greenhouse Gases

The relationship between the production of crops and emission of the greenhouse gases is rarely studied in the previous works, which deserves more attention. In this study, four types of greenhouse gases (i.e. carbon dioxide, carbon monoxide, ozone, and sulfur dioxide) and four types of crops (i.e. rice, wheat, maize, and soybean) are employed to find the correlation between them. To be more specific, this paper used least squares approximation to estimate the trend of greenhouse emission and crop production yield starting from 2000 to 2020 during the experiments. Finally, the experimental result indicated that carbon dioxide emissions increased by 100% when wheat production decreased by 130.774%. In addition, the emission of ozone also has a certain impact on the production of crops. It can be seen that the relationship between the production of crops and emission of the greenhouse gases can be established well and may benefit future agricultural production.

Effects of Deficit Irrigation and Fertigation on Growth, Yield and Water Productivity of Greenhouse-Grown Cucumber

Low water productivity has caused the wastage of water in agricultural production. Water productivity can be increased by using drip irrigation for vegetables on raised beds in greenhouses. For this purpose, a greenhouse study was conducted on sandy loam soil at the Experimental Research Station of the Asian Institute of Technology, Thailand. The experiment was designed in randomized complete block design having ten treatments using three irrigation levels [100%, 80%, 60% of actual evapotranspiration (ETc)] and three recommended dose of fertilizer (RDF) levels [100%, 80%, 60%]. Furrow irrigation with 100% of RDF was used as a control treatment. Data of crop and water productivity were recorded; the results showed that treatment T5 (W80%ETc F80%RDF) under deficit irrigation and fertigation recorded the highest mean fresh fruit yield of 66.71 t/ha while the full irrigation treatments T1 (W100% ETc F100% RDF) and T2 (W100%ETc F80%RDF) recorded the lowest mean fresh fruit yield of 12.9 t/ha and 13.9 t/ha, respectively, as against 9.6 t/ha for the control plot. Also, T5 recorded the highest water productivity of 14.47 kg/m3 while full irrigation treatments (T1 and T2) recorded the lowest water productivity of 2.52 kg/m3 and 2.73kg/m3, respectively, as against 1.37 kg/m3 for the control plot. A deficit irrigation level of 80% of ETc with a fertilizer level of 80% of RDF was found most suitable for sandy loam soil to improve crop growth, yield and water productivity for greenhouse-grown cucumber under the drip irrigation system. The results also suggest that a 20% deficit irrigation and fertilizer approach may be a good strategy for increasing water productivity when full irrigation is not possible due to limited water supplies.

Improvement of water and crop productivity of silage maize by irrigation with different levels of recycled wastewater under conventional and zero tillage conditions

Although wastewater irrigation has a long history involving different stages of development, the future expectation to improve crop yield and water productivity in wastewater irrigation practices is still relevant. Therefore, the aim of this study is to evaluate water productivity, yield and crop quality of silage maize irrigated with different application levels (100%, 67%, and 33% of irrigation need) of recycled wastewater with three replicates in conventional tillage and zero tillage practices and comparing them with full fresh water irrigation. The results of two-year field studies showed that zero tillage resulted in saving 10.1% of irrigation water on average compared to conventional tillage and actual evapotranspiration in silage maize with zero tillage was 7.4% less. Biomass yield was the highest in full irrigation with wastewater under zero tillage. Zero tillage practice provided 12.0% and 13.2% higher water productivity(WP) and irrigation water productivity(WPirrig), respectively compared to conventional tillage. The WP was higher in treatments with wastewater with 100% and 67% irrigation levels, while the WPirrig was higher in treatment with wastewater with 67% irrigation level. While the crop total soluble solids, nutrients, crude protein, chlorophyll(SPAD), leaf area index and leaf relative water content (LRWC) values were higher in full irrigation with wastewater, acid and neutral detergent fibers and membrane damage was lower. Crude protein in zero tillage was lower than in conventional tillage, while SPAD and LRWC were higher. It could be concluded that wastewater can increase silage maize biomass yield and crop quality. This result may be more valuable in terms of both protecting scarce fresh water resources and contributing to the environment with waste disposal. Moreover, 67% irrigation treatment with wastewater can be preferred for the higher WP and WPirrig values, and zero tillage can be used as a practical treatment that contributes to the yield and water saving.

Leaf transcriptome analysis of Medicago ruthenica revealed its response and adaptive strategy to drought and drought recovery

BackgroundDrought is one of the main causes of losses in forage crop yield and animal production. Medicago ruthenica (L.) cv. Zhilixing is a high-yielding alfalfa cultivar also known for its high tolerance to drought. We analyzed the transcriptome profile of this cultivar throughout drought stress and recovery and we were able to describe its phased response through the expression profiles of overlapping gene networks and drought-specific genes.ResultsThe ABA and auxin signal transduction pathways are overlapping pathways in response to drought and drought recovery in forage crops. Medicago ruthenica (L.) cv. Zhilixing adopts different strategies at different degrees of drought stress. On the 9th day of drought, transcriptional regulations related to osmoregulation are enhanced mainly through increased activities of carbohydrate and amino acid metabolism, while photosynthetic activities were reduced to slow down growth. With drought prolonging, on the 12th day of drought, the synthesis of proline and other stored organic substances was suppressed in general. After recovery, Medicago ruthenica synthesizes flavonoids through the flavonoid biosynthesis pathway to remove accumulated ROS and repair the oxidative damage from water stress. In addition, the regulation of circadian rhythm seems to accelerate the drought recovery process.ConclusionsMedicago ruthenica adapts to drought by regulating the osmoregulatory system and photosynthesis, which appears to involve the ABA and auxin signaling pathways as key regulators. Furthermore, the synthesis of flavonoids and the regulation of the circadian rhythm can accelerate the recovery process. These results enriched our knowledge of molecular responses to drought and drought recovery in Medicago ruthenica and provide useful information for the development of new legume forage grass varieties with improved adaptability to drought stress.

Crop Yield Prediction by Integrating Et-DP Dimensionality Reduction and ABP-XGBOOST Technique

In the Indian economy, one of the most significant sectors is agriculture, which is the primary source of livelihood. In precision agriculture, the major intention is to ameliorate Crop Yield (CY) production along with quality by mitigating operational costs along with environmental pollution. To attain precision agriculture, Crop Yield Prediction (CYP) is highly significant. However, CYs become unpredictable owing to their reliance on huge dimension features like soil, climate, pesticides, diseases, et cetera. Therefore, for CYP, a Feature Selection (FS)-centric Machine Learning (ML) methodology is desired to provide precision agriculture along with mitigate the computational time. An Ensemble Threshold-centric Data Perturbation (DP) FS (ET-DPFS) dimensionality reduction centered Alpha-Beta Pruning centered Extreme Gradient Boosting (ABP-XGBOOST) CYP methodology is developed here. In this model, highly pertinent candidate features, which contain higher relevance regarding the class, lower redundancy amongst the features being selected, along with stay strong against noisy data, are selected. In the proposed work, firstly, by dealing with Nan values, missing values, outliers, categorical features, along with date-time variables, an exploratory evaluation is conducted over the data. After that, by utilizing the Analysis of Variance (ANOVA) test, the redundant features are taken away. Then, by employing the ET-DPFS, the relevant features are selected. ‘’3’ base learner FS methodologies like Median Absolute Deviation-centric LASSO (MAD-LASSO), Coefficient Vector-centric Mayfly Optimization (CV-MO), and HE weight initialization-centered Relief (HEwint-Relief) are included in this ET-DPFS. Lastly, by deploying the ABP-XGBOOST, the features being selected are trained along with verified. Experiential evaluation displays that the proposed methodology is highly reliable than the prevailing methodologies by achieving precise prediction with lower Values for Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

Improved effects of combined application of nitrogen-fixing bacteria Azotobacter beijerinckii and microalgae Chlorella pyrenoidosa on wheat growth and saline-alkali soil quality

Soil salinization seriously affects crop yield and soil productivity. The application of bacteria and microalgae has been considered as a promising strategy to alleviate soil salinization. However, the effect of bacteria-microalgae symbiosis on saline-alkali land is still unclear. This study evaluated the effects of Azotobacter beijerinckii, Chlorella pyrenoidosa, and their combined application on the wheat growth and saline-alkali soil improvement. The results showed that, among all the treatments, A. beijerinckii + live C. pyrenoidosa combined inoculation group (BA) had the best effect on increasing wheat plant biomass, improving salt tolerance, and improving soil fertility. The dry weight of wheat plant in the BA group increased by 66.7%, 17.4%, and 35.0%, respectively, compared with the control group (CK), A. beijerinckii inoculation group (B), and live C. pyrenoidosa inoculation group (A). The total nitrogen content of wheat plant in the BA group increased by 69.5%, 76.7%, and 71.1%, compared with the CK, B, and A group. The proline content of wheat plant in the BA group was 100% higher than that in the CK group. The N/P ratio and K/Na ratio of wheat plant increased by 157% and 12.9% in the BA group compared with the CK group, respectively, which was more conducive to alleviating nitrogen limitation and salt stress. The A. beijerinckii + live C. pyrenoidosa inoculation treatment better reduced soil pH and improved the availability of phosphorus in soil. This study illustrated the comprehensive application prospects of bacteria-microalgae interactions on wheat growth promotion and soil improvement in saline-alkali land, and provided a new effective strategy for improving saline-alkali soil quality and increasing crop productivity.

Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia.

The online version contains supplementary material available at 10.1007/s42106-022-00223-2.

A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization

Climate change and population growth risk the world’s food supply. Annual crop yield production is one of the most crucial components of the global food supply. Moreover, the COVID-19 pandemic has stressed global food security, production, and supply chains. Using biomass estimation as a reliable yield indicator, space-based monitoring of crops can assist in mitigating these stresses by providing reliable product information. Research has been conducted to estimate crop biophysical parameters by destructive and non-destructive approaches. In particular, researchers have investigated the potential of various analytical methods to determine a range of crop parameters using remote sensing data and methods. To this end, they have investigated diverse sources of Earth observations, including radar and optical images with various spatial, spectral, and temporal resolutions. This paper reviews and analyzes publications from the past 30 years to identify trends in crop monitoring research using remote sensing data and tools. This analysis is accomplished through a systematic review of 277 papers and documents the methods, challenges, and opportunities frequently cited in the scientific literature. The results revealed that research in this field had increased dramatically over this study period. In addition, the analyses confirmed that the normalized difference vegetation index (NDVI) had been the most studied vegetation index to estimate crop parameters. Moreover, this analysis showed that wheat and corn were the most studied crops, globally.

Animal-derived plant biostimulant alleviates drought stress by regulating photosynthesis, osmotic adjustment, and antioxidant systems in tomato plants

• Pig blood-derived protein hydrolysate alleviated negative effects of drought stress on tomato growth. • Pig blood-derived protein hydrolysate improved chlorophyll content and photosynthetic capacity. • Pig blood-derived protein hydrolysate enhanced accumulation of proline, soluble proteins, soluble sugars, and inorganic ions. • Pig blood-derived protein hydrolysate reduced drought-induced oxidative damage by stimulating antioxidant systems. Drought stress is one of the major abiotic factors limiting crop growth and yield production. Protein hydrolysates have been used as plant biostimulants in agriculture due to their positive impacts on plant productivity under abiotic stress; however, little is known about their roles in inducing drought tolerance and the underlying mechanisms. Therefore, we investigated the effects of a new pig blood-derived protein hydrolysate (PP) in increasing tomato tolerance to drought stress. We found that foliar PP application reduced the inhibited impacts of drought stress (10% PEG-6000) on tomato growth, as indicated by improved plant growth parameters. Exogenous PP application inhibited the degradation of chlorophyll, maintained chloroplast structures, increased stomatal aperture, and thereby improved photosynthetic rate under drought stress. The higher accumulation of proline, soluble sugars, soluble proteins, inorganic ions including K + , Ca 2+ , and Mg 2+ , and subsequently higher relative water content were observed in PP-treated tomato leaves and roots. Moreover, PP application mitigated oxidative damages of drought affected-tomato plants by increasing the activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) and the accumulation of total phenolic, total flavonoid, ascorbic acid, and glutathione. These findings indicated that foliar PP application could obviously alleviate drought stress by regulating stomatal aperture, chloroplast ultrastructure, osmotic alteration, and antioxidant systems. Therefore, protein hydrolysate derived from animal protein is an effective, economical, and environmental plant biostimulant for improving plant performances under drought stress.

Rotation of Triticale and Sweet Sorghum Improves Saline-Alkali Soil and Increases Productivity in a Saline Soil

ABSTRACT Soil salinization significantly restricts crop yield, animal foraging, and biomass production for biofuels. Identifying an efficient farming system to mitigate these concerns has become a major research focus. The present study addresses the potential of rotating triticale and sweet sorghum at various planting densities to alleviate soil salt stress, enhance production, improve agronomic characteristics, and boost soil microbial communities in a saline agricultural environment in East China. Several indicators in 2017 and 2018. Results show that triticale and sweet sorghum planting significantly decreased soil salt contents compared to bare land plots. Multiple agronomic traits were measured for each planting density. For triticale, a density of 180 × 104 plants ha−1 showed the best plant height, tiller number per plant, yield, and lodging rate; for sweet sorghum, a density of 7 × 104 plants/ha showed the best plant height, yield, and lodging rate. High-density planting of triticale (180, 260, 320 × 104 plants/ha) and sweet sorghum (7, 8, 9 × 104 plants/ha) was associated with high counts of bacteria, Actinomycetes, and salt-tolerant Actinomycetes. However, planting density had no significant effect on the counts of fungi or salt-tolerant bacteria. Redundancy Discriminant Analysis (RDA) showed that plant height, yield, lodging rate, bacteria, salt-tolerant bacteria, Actinomycetes, and salt-tolerant Actinomycetes were negatively correlated with soil salinity, while tiller number per plant and stem thickness were positively correlated with soil salinity. In conclusion, a rotation system of triticale and sweet sorghum with planting densities of 180 × 104 and 7 × 104 plants/ha, respectively was established to improve and make full use of saline soil.

High-resolution crop yield and water productivity dataset generated using random forest and remote sensing

Accurate and high-resolution crop yield and crop water productivity (CWP) datasets are required to understand and predict spatiotemporal variation in agricultural production capacity; however, datasets for maize and wheat, two key staple dryland crops in China, are currently lacking. In this study, we generated and evaluated a long-term data series, at 1-km resolution of crop yield and CWP for maize and wheat across China, based on the multiple remotely sensed indicators and random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. Our analyses, which showed spatiotemporal patterns of maize and wheat yields and CWP across China, can be used to optimize agricultural production strategies in the context of maintaining food security.

High-Throughput Phenotyping of Cross-Sectional Morphology to Assess Stalk Mechanical Properties in Sorghum

Lodging is one of the major constraints in attaining high yield in crop production. Major factors associated with stalk lodging involve morphological traits and anatomical features along with the chemical composition of the stem. However, little relevant research has been carried out in sorghum, particularly on the anatomical aspects. In this study, with a high-throughput procedure newly developed by our research group, the nine parameters related to stem regions and vascular bundles were generated in 58 sorghum germplasm accessions grown in two successive seasons. Correlation analysis and principal component analysis were conducted to investigate the relationship between anatomical aspects and stalk mechanical traits (breaking force, stalk strength and lodging index). It was found that most vascular parameters were positively associated with breaking force and lodging index with the correlation coefficient r varying from −0.46 to 0.64, whereas stalk strength was only associated with rind area with the r = 0.38. The germplasm resources can be divided into two contrasting categories (classes I with 23 accessions and II with 30 accessions). Compared to class II, the class I was characterized by a larger number (+40.7%) and bigger vascular bundle (+30%), thicker stem (+19.6%) and thicker rind (+36.0%) but shorter internode (plant) (−91.0%). This study provides the methodology and information for the studies of the stem anatomical parameters in crops and facilitates the selective breeding of sorghum.

Rice and Wheat Yield Prediction in India Using Decision Tree and Random Forest

One of the main sources of revenue and growth in Indian economy is from agriculture. It is often a gamble for the farmers to obtain a decent yield, considering the unpredictable environmental conditions. This paper deals with the prediction of the yield of rice and wheat using machine learning algorithms using the annual crop yield production and the annual rainfall in the different districts of India. In this paper, a popular prediction model is developed using algorithms such as decision tree and random forest to predict the yield of most widely grown crops in India like rice and wheat. The features used were the area of production, rainfall, season and state. The season and the state were one hot encoded features. Mean square error was used to measure the loss. The dataset was prepared by combining the crop production in the various states and the rainfall dataset in the respective states. Index Terms : Machine Learning, XGBoost, Decision Tree, Random Forest, Data Preprocessing, Data Visualization, Prediction