Maize Harvest Research Articles

Research on Mass Prediction of Maize Kernel Based on Machine Vision and Machine Learning Algorithm

The yield assessment process during maize harvesting is a necessary means to ensure farmers’ economic benefits and stable agricultural production. Predicting the mass of maize kernels is an important condition for yield detection. This study proposes a maize kernel mass prediction model based on machine vision and machine learning algorithms to determine whether the kernels are broken. By extracting the geometric features of maize kernels, a phenotypic feature dataset of maize kernels was constructed. Subsequently, popular machine learning algorithms were used to establish regression models for maize kernel mass, achieving quantitative prediction of maize kernel mass. The results indicate that the PLSR (Partial Least Squares Regression) and RF (Random Forest) algorithms are suitable for constructing mass prediction models for broken and unbroken kernels, respectively. The models established by the two algorithms achieved R-values of 0.941 and 0.925, respectively. Field trial results show that there is a strong linear relationship between the predicted maize kernel mass using the constructed model and the actual kernel mass. Therefore, this method can serve as an accurate, objective, and efficient detection method for maize yield.

Effects of Different Nitrogen Topdressing Ratios on Soil Nitrate-Nitrogen and Summer Maize Growth

Abstract: The Jianghuai region in China is well known for its high-quality and high-yielding maize production, but there is inadequate analysis about the N management in this region (especially the topdressing ratio). To evaluate the suitable topdressing ratio for maize nitrogen application, the effects of different nitrogen topdressing ratios on soil nitrogen and summer maize growth were studied in 2022–2023. In each treatment, a total of 250 kg N/hm2 was applied, i.e., 50 kg N/hm2 was applied as the base fertilizer, and the rest of the nitrogen fertilizer (200 kg N/hm2) was applied at the jointing and filling stages at different ratios, including 3:7 (60 and 140 kg N/hm2 were applied at the jointing and filling stages, respectively, T1 treatment), 7:3 (T2 treatment), and 1:1 (CK treatment). Compound fertilizer (N: P: K = 18%: 12%: 5%) was used as the base fertilizer, and urea was used as the topdressing fertilizer. The results showed that in 2022 (dry year), compared with values in the T1 treatment, the nitrate-nitrogen accumulation in the 0–100 cm soil layer at maize harvest under the T2 and CK treatments decreased by 33.8% and 14.7%, respectively; compared with values in CK treatment, the T2 treatment could significantly increase the ear length of maize by 9.4%. In 2023 (wet year), compared with values in the T1 treatment, the 100-grain weight, maize yield, N partial factor productivity (NPFP), and N uptake efficiency (NUPE) of T2 treatment significantly increased by 13.4%, 17.2%, 20.1%, and 21.5%, respectively; compared with values in the CK treatment, ear length, maize yield, and NPFP of T2 treatment significantly increased by 6.15%, 14.0%, and 15.8%, respectively. Therefore, for this study, a topdressing ratio of 7:3 between the jointing and filling stages (T2 treatment) was beneficial to reduce nitrogen accumulation in dry years and increase maize yield and nitrogen partial factor productivity in wet years.

Antioxidant, Enzyme-Inhibitory and Antimicrobial Activity of Underutilized Wheat and Maize Crop Residues

Global wheat and maize production, which reached two billion tonnes in 2021, generates significant agricultural waste with largely untapped potential. This study investigates the bioactive properties of ethanol extracts from wheat and maize harvest residues, their ethyl acetate fractions, and their principal compounds. In vitro assays (DPPH, ABTS, FRAP, and TRC) revealed variable antioxidant capacities among the samples, with ferulic acid demonstrating the strongest free-radical scavenging and reducing effects, often surpassing those of standard antioxidant controls. Enzyme inhibition assays identified the flavonoid tricin as the most effective inhibitor of α-glucosidase, acetylcholinesterase, and butyrylcholinesterase, while the flavonolignan mixture of salcolins A and B showed the highest inhibitory activity against α-amylase and tyrosinase. Antimicrobial testing using the broth microdilution method resulted in minimum inhibitory concentrations (MICs) ranging from 31.25 µg/mL to >1000 µg/mL. Gram-positive bacteria showed the highest susceptibility, Candida albicans exhibited variable sensitivity, and Gram-negative bacteria were resistant in the tested concentration range. Bioactivity increased in the order of extracts, fractions, and then individual compounds. These findings suggest that wheat and maize residues possess notable bioactive properties, highlighting their potential as sources of valuable and pharmacologically active compounds.

Advances and limitations in maize harvesting technologies: An overview

Advances and limitations in maize harvesting technologies: An overview

Examining the Impact of Microbial Compost from Anaerobic Digestion on Soil Fertility and Maize Crop Nutrient Uptake

This research study was to evaluate the effects of combining microbial compost and mineral fertilizer on soil properties, maize growth, and nutrient uptake. Therefore, after selecting normal soil, 10 kg of soil was placed in each pot. Nine treatments with three replications were applied by using a Completely Randomized Design (CRD) for the study layout. The results revealed that the maximum plant height (101.73 cm), shoot fresh weight (69.36 g), shoot dry weight (128.6 g), root fresh weight (1.68 g), and root dry weight (0.89 g), as well as the highest content of nitrogen (1.66%), the highest phosphorus concentration (1.04%), and the maximum potassium concentration (2.13%) were noted in SF+MM + ½ NPK, while contents of iron (80.2 mg/kg), zinc (98.46 mg/kg), copper (78.66 mg/kg), and manganese (67.7 mg/kg) were also recorded in SF+MM + ½ NPK compared to other treatments. After harvesting maize crops, the lowest pH (7.27), highest EC (0.38 dS/M), and the highest contents of organic matter (1.03%) were recorded in SF+MM + ½ NPK. Maximum nitrogen content in soil (37 mg/kg), phosphorus content in soil (19.7 mg/kg), and potassium content in soil (105.8 mg/kg) were recorded in T8, while maximum contents of iron (4.88 mg/kg), zinc (1.80 mg/kg), copper (0.51 mg/kg), and manganese (1.95 mg/kg) were recorded in SF+MM + ½ NPK. The combination of SF+MM + ½ NPK showed to be the most effective treatment, whereas the usage of compost and chemical fertilizer alone remained the least effective.

The effect of mycorrhiza-inoculated succession-planted main crop (maize-wheat) and cover crop rotation on soil organic carbon

There has long been intense pressure on input-based traditional agriculture due to the rising need for food. These kinds of agricultural techniques result in poorly managed soil-plant interactions that ultimately affect all living activities and human health by altering and destroying the soil's natural structure, rendering it infertile, and degrading soil quality. Research was carried out a study to find out how mycorrhizae-inoculated cover crops affected soil quality and growth metrics. The study to pot experiment was carried out under greenhouse conditions. Five different plant combinations (cover crop) patterns were inoculated with selected and indigenous mycorrhizae spores that were isolated from the rhizosphere soils of plants grown in three degraded soils. Five different cover plant combinations, such as A: Clover, Grass, Onion; B: Faba Bean, Grass, Safflower; C: Clover, Grass, Safflower, Faba Bean, Onion; D: Maize; and E: Wheat, were planted in 7 kg soil-containing pots with three replications. The seeds of different plants were planted per m2 surface of each pot, with the number of seeds per pot calculated depending on their sowing amount per hectare. Sixty days after planting, the plants were harvested by cutting 0.5 cm above the soil surface with scissors. In the second rotation experiment, after the harvest of the first rotation experiment, maize (Zea mays L.) was planted on the A, B, C, and D patterns, and wheat was planted on the E pattern. In the third rotation, wheat was sown on all pots after the second maize harvest. As a result of successive three-pot experiments on the same soils, were analyzed for the soil organic carbon (SOC) concentrations. In general, treatments with Funneliformis mosseae followed by indigenous mycorrhiza spores led to an increase in soil OC concentration compared to control treatments. SOC concentrations in Havutlu and Arık soils were higher than those in Avadan soil. Avadan is a highly eroded soil and has high lime content, high pH, and low soil fertility. When consecutively planted trials are examined, the soil SOC concentration increased from the first trial to the last trial for all soils. The obtained SOC results seem to partially support the research hypothesis. The results revealed that poorly managed soil requires rehabilitations with various combinations of cover crops.

Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms.

Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. A new dataset of high moisture content maize kernel phenotypic features was constructed by extracting seven features (geometric and shape features). Then, the regression model of the kernel (broken and unbroken) weight prediction and the classification model of kernel defect detection were established using the mainstream machine learning algorithm. In this way, the defect rapid identification and accurate weight prediction of broken kernels achieve the purpose of broken rate quantitative detection. The results prove that LGBM (light gradient boosting machine) and RF (random forest) algorithms were suitable for constructing weight prediction models of broken and unbroken kernels, respectively. The r values of the models built by the two algorithms were 0.985 and 0.910, respectively. SVM (support vector machine) algorithms perform well in constructing maize kernel classification models, with more than 95% classification accuracy. A strong linear relationship was observed between the predicted and actual broken rates. Therefore, this method could help to be an accurate, objective, efficient broken rate online detection method for maize harvest.

Assessment of extreme climate stress across China’s maize harvest region in CMIP6 simulations

Climate change is expected to increase the frequency and severity of climate extremes, which will negatively impact crop production. As one of the main food and feed crops, maize is also vulnerable to extreme climate events. In order to accurately and comprehensively assess the future climate risk to maize, it is urgent to project and evaluate the stress of extreme climate related maize production under future climate scenarios. In this study, we comprehensively evaluated the spatio-temporal changes in the frequency and intensity of six extreme climate indices (ECIs) across China’s maize harvest region by using a multi-model ensemble method, and examined the capability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to capture these variations. We found that the Independence Weight Mean (IWM) ensemble results calculated by multiple Global Climate Models (GCMs) with bias correction could better reproduce each ECI. The results indicated that heat stress for maize showed consistent increase trends under four future climate scenarios in the 21st century. The intensity and frequency of the three extreme temperature indices in 2080s were significantly higher than these in 2040s, and in the high emission scenario were significantly higher than these in the low emission scenario. The three extreme precipitation indices changed slightly in the future, but the spatial changes were more significant. Therefore, with the uncertainty of climate change and the differences of climate characteristics in different regions, the optimization of specific management measures should be considered in combination with the specific conditions of future local climate change.

Genomic Prediction of Kernel Water Content in a Hybrid Population for Mechanized Harvesting in Maize in Northern China

Genomic prediction enables rapid selection of maize varieties with low kernel water content (KWC), facilitating the development of mechanized maize harvesting and reducing costs. This study evaluated and characterized the KWC and grain yield (GY) of hybrid maize in northern China and used genomic prediction to identify superior hybrid combinations with low kernel water content at maturity (MKWC) and high GY adapted to northern China. A total of 285 hybrids obtained from single crosses of 34 inbred lines from Stiff Stalk and Non-Stiff Stalk heterotic groups were used for genomic prediction of KWC and GY. We tested 20 different statistical prediction models considering additive effects and evaluating the impact of dominance and epistasis on prediction accuracy. Employing 10-fold cross-validation, it showed that the average prediction accuracy ranged drastically from 0.386 to 0.874 across traits and models. Eight linear statistical methods displayed a very similar prediction accuracy for each trait. The average prediction accuracy of machine learning methods was lower than that of linear statistical methods for KWC-related traits, but the random forest model had a high prediction accuracy of 0.510 for GY. When genetic effects were incorporated into the prediction model, the prediction accuracy for each trait was improved. Overall, the model with dominant and epistatic effects (G:AD(AA)) performed best. For the same number of markers, predictions using trait-specific markers resulted in higher prediction accuracy than randomly selected markers. When the number of trait-specific SNPs was set to 100, the prediction accuracy of GY increased by 33.27%, from 0.406 to 0.541. Out of all the 561 potential hybrids, the TOP 30 hybrids selected by genomic prediction would lead to a 1.44% decrease in MKWC compared with Xianyu335, a hybrid with a fast kernel water dry-down, and these hybrids also had higher GY simultaneously. Our results confirm the value of genomic prediction for hybrid breeding low MKWC suitable for maize mechanized harvesting in northern China. In conclusion, this study highlights the potential of genomic prediction to optimize maize hybrid breeding, enhancing efficiency and providing insights into genotype-accuracy relationships. The findings offer new strategies for hybrid design and advancing mechanized harvesting in northern China.

Practices of maize handling and nixtamalization to reduce fungal toxin exposure in rural Guatemala

Fungal toxins in local food supplies are a critical environmental health risk to communities globally. To better characterize hypothesized toxin control points among households, we conducted household surveys across four departments (first administrative division) in Guatemala. Data gathered included maize harvesting, processing, storage, and traditional nixtamalization practices. In total, n = 33 households participated in the survey and were from 4 unique departments, 17 unique municipalities, and represented 4 different languages. The results suggested that the majority of households consumed a combination of personally cultivated and purchased maize. There was significant variation in how this maize was stored, in regards to pre-processing (kernel vs whole cob), as well as storage system type. For nixtamalization, the largest differences in practices (e.g., cooking time) were based on household size while the majority of households reported practices that aligned with previously reported best practices. Lastly, all reported maize-based food products produced by households utilized the nixtamalization process except one. Current maize handling and nixtamalization practices reported by a majority of households aligned with best practices, however, with locally tailored and culturally sensitive guidance disseminated by key stakeholders, the prevalence of best practice use among households can be improved. Further community-based research on traditional farming and nixtamalization practices can improve these recommendations.

Prevalence and abundance of plant-parasitic nematodes in New Zealand maize fields: effects of territory, soil orders, crop stage, and sampling time

ABSTRACT Plant-parasitic nematodes (PPNs) are significant agricultural pests that can reduce maize yields. This study examines the prevalence, abundance, and diversity of PPNs in New Zealand maize fields, focusing on the effects of territory, soil orders, crop stages, and sampling times. Seven PPN genera were identified: Pratylenchus spp. (root-lesion), Helicotylenchus spp. (spiral), Meloidogyne spp. (root-knot), Heterodera spp. (cyst), Paratylenchus spp. (pin), Criconemella spp. (ring), and Tylenchus spp. PPNs were present in 98% of the samples, with Pratylenchus spp. being the most prevalent (91%), followed by Helicotylenchus spp. (38%). Compared to Waikato and Manawatu-Whanganui, Canterbury had the highest nematode populations, particularly of Pratylenchus spp. and Helicotylenchus spp. Brown and pallic soils supported higher PPN abundances. Sampling during the maize harvesting stage and late autumn resulted in the highest nematode populations and diversity indices. Pratylenchus spp. populations often exceeded the economic threshold of 500 Pratylenchus kg−1 of soil, suggesting a significant threat to maize yield in New Zealand. The findings highlight the need for further research to assess the impact of Pratylenchus spp. on maize yield and to develop effective management practices for maize cultivation in the country.

Design and test of real-time monitoring system for maize entrainment loss based on piezoelectric signal classification

This study describes a novel entrainment loss monitoring system. The features of the collision signals of different maize materials were extracted in the time and frequency domains, and a kernel loss identification model was constructed using machine learning algorithms. The results show that the random forest model performed optimally, with classification accuracies of 96.83% and 94.85% on the training and test sets, respectively. An entrainment loss monitoring system was developed, and the optimal sensor location was determined. The accuracy of kernel classification was 91.09% and that of cob classification was 86.69% for the kernel/cob mixture. Field validation tests showed that the proposed entrainment loss monitoring system outperformed the traditional monitoring system. The method enables the precise monitoring of entrainment losses during maize harvesting, enabling intelligent maize production.

DETERMINANTS OF FARMER’S PERCEPTION OF PROSTEPHANUS TRUNCATUS (HORN) DAMAGES AND LOSSES ON STORED MAIZE IN TOGO AND BENIN (WEST AFRICA)

Prostephanus truncatus is one of the most damaging post-harvest pests of stored maize in Africa. Research Institutions disseminated Various control strategies during the 1990s in West Africa with significant results. What is farmers’ perception of this pest in decades after those control strategies? This study aimed to assess determinants of farmers’ perception of current damages and losses caused by Prostephanus truncatus on stored maize in southern of the two countries (Benin and Togo). 300 maize farmers (150 from Togo and 150 from Benin) were randomly selected from 10 villages per country. Descriptive statistics were used in addition to Logit and Tobit regression analysis. The results showed that 38% of farmers (35% in Togo and 41% in Benin) still perceive Prostephanus truncatus as the most damaging pest of stored maize. Logit's results showed that factors influencing farmers’ perception of Prostephanus truncatus were group membership, contact with extension agents, and period of maize harvesting. The results from the Tobit regression model showed that factors such as the maize variety, the form of maize storage (with husk), and the storage period influence the extent of damage and loss caused by Prostephanus truncatus. Farmers still perceive Prostephanus truncatus as the main post-harvest pest causing high damage and losses on maize. It is recommended that further dissemination of maize storage technology to reduce postharvest losses.

Inovasi Teknologi dalam Upaya Peningkatan Nilai Jual melalui Diversifikasi Produk Olahan Jagung pada Kelompok Tani di Kabupaten Soppeng

Maize commodities in Watu toa Village are still widely managed as raw materials for animal feed and are generally marketed in primary form either in the form of dried corn flakes or sold in the form of fresh corn at low prices. This is due to the limited knowledge of the community in applying technology to develop the potential of maize, resulting in low income for farmers. Maize price Daily transactions Maize is traded at USD490.25 per bushel. In rupiah terms, this is equivalent to IDR 2,992.5 per kg. Despite the abundant maize harvest in Watutoa village, this has not been able to increase farmers' income. This is because the processing of corn for selling value is still very limited where farmers only sell their corn agricultural products directly at low prices and on the other hand the marketing method is also still limited to the area around Watutoa village. to help solve the existing problems, community service is carried out to apply diversified corn processing technology. The purpose of the implementation of the service activities carried out is to increase the value of processed corn production into selling value by verifying processed corn based on the application of appropriate technology. The implementation methods carried out are socialisation, training, evaluation and mentoring. The results of the community service activities of corn processed diversification can increase community knowledge about corn diversification, increase the type of processed corn production into three processed corn in the form of popcorn, corn flour, and corn sugar. The results of processed corn diversification of 1 kg of raw materials at a price of 2500 rupiah are processed into popcorn as much as 1800 grams or 18 plastic packs of 100 grams with an increase in income from 2500 rupiah to Rp.90,000. for the results of corn flour diversification from raw materials as much as 142 grams at a price of 370 rupiah into flour as much as 100gram at a price of 4500 rupiah. And for the diversification of corn sugar from 1 Kg of corn raw material processed into liquid sugar produces 700 ML at a price of 17,000 rupiah.

QTL mapping and omics analysis to identify genes controlling kernel dehydration in maize.

This study mapped and screened three candidate genes related to kernel dehydration in maize. The slow development rate of maize kernels during later stages leads to high kernel moisture content at harvest, posing a challenge for mechanized maize harvesting in China. This study utilized a recombinant inbred line population derived from Zheng 58 (slow dehydration) and PH6WC (fast dehydration) as parents. After four years of trait investigation and analysis, 25 quantitative trait loci (QTLs) associated with kernel dehydration rate and moisture content were identified, with six QTLs showing a significant contribution value exceeding 10% in the phenotype. Furthermore, a comparison was made between the QTLs identified in this study and those from previous research on maize kernel moisture content and dehydration rate, followed by screening through the omics analysis of the parental lines. Three candidate genes related to kernel dehydration rate were identified, primarily involving carbohydrate metabolism, energy metabolism processes (Zm00001d014030 and Zm00001d006476), and stimulus resistance (Zm00001d040113). These findings provide valuable insights to assist and guide future breeding efforts for mechanical harvesting of maize.

Development of a low-damage maize threshing system based on discrete element technology to effectively improve maize harvest quality and yield

During maize harvesting, the mechanical action and suboptimal design of threshing systems often lead to kernel breakage and loss, which can reduce both maize quality and yield. This study introduces a novel rasp bar threshing element designed to minimize kernel loss. The effects of the structural parameters of this threshing element on its performance are analyzed using a flexible discrete element model of maize. Moreover, the performance of the novel threshing element is compared to that of a traditional threshing element, and the simulation results are verified through experimental tests. Test data shows that, compared to the traditional threshing element, the novel threshing element results in a relative decrease of 26.94 %, 21.95 %, and 27.05 % in the broken rate, unthreshed rate, and the entrainment loss rate, respectively. Finally, this research provides technical support for high-quality, low-loss maize production.

Detection of kernels in maize forage using hyperspectral imaging

In this study, the use of hyperspectral imaging was examined to enhance the kernel processing assessment of maize forage, which is crucial for optimizing the production of dairy cow feed and biogas production. As the visual contrast between the kernels and other crop particles is quite limited, spectral imaging could provide better kernel classification. A pushbroom hyperspectral imaging system was used, scanning samples collected during maize harvesting in the 400–1000 nm range. A PLSDA model for pixel classification was developed to distinguish kernel spectra from the other particles in the forage, achieving a pixel-level classification accuracy of 95.2 %. Next, the most important wavelengths were identified by means of a stepwise procedure using the Wilks Lambda criterion. A Pixel classification using the top five discriminating wavelengths achieved nearly the same accuracy as the full spectrum wavelength model (95.2 % compared to 93.5 %) and did considerably better than the RGB classifier’s 86.3 % accuracy. Finally, these top 5 discriminating wavebands were applied in an object detection deep learning model, more specifically a Faster R-CNN model. While object detection based on these 5 wavebands stilled outperformed object detection based on RGB wavebands, detection performance, as measured by AP50, was rather low. This weak performance resulted from the low resolution of the hyperspectral imaging camera.

Cereal-maize vs. legume-maize double-cropping: Impact on crop productivity and nitrogen dynamics under flood-irrigated Mediterranean conditions

Double-cropping is an interesting diversification strategy to increase profitability and sustainability. Besides, by including legumes, it may reduce the N inputs dependency. However, its management with flood irrigation can be challenging. The was the evaluation of the impact of two double-cropping and different nitrogen (N) rates on productivity and N dynamics under flood irrigation. Two double-cropping (barley-maize; pea-maize) and three N rates (unfertilized, 0N; medium: MN; high rate, HN) were evaluated in a field experiment located in NE Spain during two years. In barley-maize, N rates were 0, 125 and 250 kg N ha−1, and 0, 200 and 400 kg N ha−1 for barley and maize phases, respectively. In pea-maize, pea did not receive N, and the MN and HN rates in the maize were reduced (150 and 350 kg N ha−1, respectively).Crop productivity and N uptake were evaluated. The economic return and N use efficiency (NUE) were calculated. Soil inorganic N (Nmin) was measured after harvest.The pea-maize double-cropping allowed the saving of N fertilizer without yield penalties. Besides, a pre-crop effect of pea in the maize yield was observed. Regarding the cropping system, pea-maize obtained a higher economic return the first year. Besides, the economic return was higher the first year, due to crop penalties. In general, NUE was greater in 0N, and no differences were observed between fertilized treatments. Soil Nmin after pea/barley was higher the first year, coincident with a higher productivity. After maize harvest, the barley-maize led to larger residual Nmin.Therefore, in this study, pea-maize allowed a reduction in the N rate, showed greater potential to increase the economic return and reduced the N leaching risk. However, crop yield penalties associated with flood-irrigation, indicate that farmers should be aware of facing a more challenging management or consider other irrigation method to ensure profitability.

Two new qPCR assays for detecting and quantifying the Aspergillus flavus and Aspergillus parasiticus clades in maize kernels

AbstractThe fungi of Aspergillus section Flavi can produce carcinogenic mycotoxins—aflatoxins (AFs)—of two types: types B and G (AFBs and AFGs). AFs are highly hazardous for human and animal health. Their levels in food and feed are therefore highly regulated, with a low acceptable limit for AF content. In France, climate change has led to the detection of AFs in maize harvests since 2015. Mycoflora analyses have identified two species, A. flavus (producing AFBs) and A. parasiticus (producing both AFBs and AFGs), as responsible for this AF contamination. However, mycoflora analysis is a time‐consuming method that cannot readily be applied to large numbers of samples. We propose here an alternative clade‐specific functional TaqMan quantitative PCR method based on the calmodulin gene for distinguishing between the A. flavus clade (AfC) and the A. parasiticus clade (ApC). We applied this method to 553 maize samples collected in three different harvest years (2018–2020). Both clades were detected in about 40% of the samples tested. As expected, we observed significant positive correlations between AFBs and AfC DNA (R2 = 0.708), and between AFGs and ApC DNA (R2 = 0.885). This method will be useful for the rapid, simple and cheap characterization of maize grain contamination with Aspergillus section Flavi. This method will make it possible to study the relationship between agroclimatic conditions, AF content and species prevalence, to facilitate the anticipation of AF risks due to global warming in France.

Development of Soil Test Crop Response (Stcr) and Stcr-Integrated Plant Nutrition Supply (Ipns) Models to Achieve Targeted Yields of Maize (Zea mays L.) in Vertisols of Northern Karnataka

Background: Soil test crop response (STCR) approach assists in determining the appropriate amount of fertilizer to use for particular season and location based on the initial soil fertility status and crop response in terms of nutrient uptake and yield. A field experiment was conducted during 2020-21 at Dharwad, by adopting the Inductive Cum Targeted Yield Model to develop soil test-based fertilizer prescriptions under STCR and STCR-IPNS approach to achieve desired target yields of maize in vertisols of Northern Karnataka, India. Methods: In the experiment’s first phase, an exhaust crop fodder maize was grown in three different fertility strips. After the harvest of fodder maize, the soil fertility levels varied significantly among the strips, indicating the creation of fertility gradients. In the second phase of the experiment, a test crop (hybrid maize) was grown and the response of maize to four levels of N, P and K and three levels of FYM under different fertility gradients was studied. The basic parameters viz, nutrient requirement (NR), contribution of nutrients from soil (Cs), fertilizers (Cf) and FYM (Cfym) were computed using the data from test crop experiment. The basic parameters viz, nutrient requirement (NR), contribution of nutrients from soil (Cs), fertilizers (Cf) and FYM (Cfym) were computed using the data from test crop experiment. Result: Using the basic parameters, fertilizer prescription equations for desired yield targets of maize under NPK fertilizers alone and under STCR-IPNS (with FYM) were derived based on initial soil test values. Results of the validation experiment in kharif 2021 revealed that the grain yield was significantly higher under STCR-IPNS equation with a target yield of 100 q ha-1 as compared to other treatments. STCR-IPNS equation with the application of FYM to achieve 100 q ha-1 target yield resulted in savings of 11, 10 and 15 percent of N, P2O5 and K2O as compared to STCR-NPK alone equation.