Yield Of Maize Crop Research Articles

Effect on biomass production and phosphorus use efficiency of maize by using citric acid amended di-ammonium phosphate fertilizer

Phosphorus (P) is a critical factor influencing maize (Zea mays L.) development and yield. To explore the influence of P on maize growth, a pot experiment was conducted during the summer of 2021. There were five treatments T1 = No DAP/CA, T2 = 5 ml (80 mg kg−1 soil) DAP, T3 = 5 ml (120 mg kg−1 soil) DAP, T4 = 5 ml (80 mg kg−1 soil) DAP and 2 mM citric acid, T5 = 5 ml (120 mg kg−1 soil) DAP and 2 mM citric acid. Citric acid-amended P fertilizers (i.e., CA and DAP) exhibited significant effects (p < 0.05) on all the parameters studied in comparison to the sole application of DAP. Among phosphorus fertilized treatments, the DAP 120 + CA produced significantly higher biomass yield (16.37 g pot−1), while sole DAP 80 produced lower biomass (10.61 g pot−1); the DAP 120 + CA produced significantly higher plant length (34.273 cm), while sole DAP 80 produced lower plant length (30.467 cm); DAP 80 + CA produced significantly higher phosphorus recovery (25.583%), while sole DAP 120 produced lower phosphorus recovery (19.863%); DAP 120 + CA showed significantly higher phosphorus use (69.565 mg plant-1), while sole DAP 80 showed lower phosphorus use (39.154 mg plant-1); the highest total PUE (40.503%) was recorded in DAP 120 + CA treatment, while sole DAP 80 treatments showed lower PUE (22.743%); and the highest PCN (4.25 mg g-1) was recorded in DAP 120 + CA treatment while sole DAP 80 treatments showed lower PCN (3.69 mg g-1). In conclusion, the use of citric acid-amended P fertilizers (CA and DAP) significantly improved all parameters studied, compared to the sole application of DAP. The DAP 120 + CA treatment showed the highest values for biomass yield, plant length, phosphorus recovery, phosphorus use, total PUE, and PCN, while the sole DAP 80 treatment showed lower values for these parameters. The findings suggest that growing maize using citric acid-amended DAP could be more beneficial to get a higher crop yield of maize. Thus, fertilizer application in maize agriculture can be a technique for boosting both economic and environmental profitability.

Integrated use of organic and synthetic fertilizers improves soil functioning, growth, yield and quality attributes of maize

The combined use of organic and chemical fertilizers may prove to be an excellent option for maintaining soil health, increasing fertilizer use efficiency, and improving crop quality and yield of maize. So, with this objective in mind a pot experiment was conducted to find out the effectiveness of organic and synthetic fertilizers on the yield and quality of maize crop. Two organic fertilizer levels (FYM and Press-mud @ 2% and 4% of each) were tested separately and in combination with recommended NPK fertilizers (160: 80: 60 kg ha-1). The experimental units were laid out in a completely randomized design (CRD) with five replications. The results showed that the combined use of NPK + press mud @ 4% caused a significant increase in grain numbers per cob (8.6%), weight of grains per cob (11.5%), grain yield, biological yield, harvest index, stalk yield, and oil and protein contents up to 11.5%, 6.3%, 19.7%, 12.6% and 12.0%, respectively as compared to the control treatment. The oil and protein contents were also improved significantly over the sole application of NPK fertilizer. Soil health was also improved due to the combined use of synthetic fertilizer with 4% press mud and 2% FYM. It is concluded that integrated use of NPK fertilizers and press mud @ 4% not only increased the quality and yield of maize, but it also improved soil health.

Hymenopteran parasitoid complex and fall armyworm: a case study in eastern India

Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) has significantly affected maize crop yields, production efficiency, and farmers’ incomes in the Indian Eastern Gangetic Plains region since it was first observed in India in 2018. A lack of awareness by maize growers of the appropriate selection, method, and timing of insecticide application not only creates a barrier to sustainable FAW control but also contributes to increased environmental pollution, reduced human health and increased production costs. We demonstrated that FAW inflicted the most damage in early whorl growth stage of maize, regardless of whether chemical insecticides were applied. FAW egg masses and larvae collected from maize fields in which no insecticides had been sprayed showed high parasitism rates by parasitoid wasps; in contrast fields that had been sprayed had much lower rates of parasitism on FAW. Ten hymenopteran parasitoids were observed in maize fields across the study region, suggesting a diversity of natural methods to suppress FAW in maize at different growth stages. These included two FAW egg parasitoids and eight FAW larval parasitoids. Microplitis manilae Ashmead was the most abundant FAW larval parasitoid species, and Telenomus cf. remus was the dominant FAW egg parasitoid species. Endemic FAW parasitoids such as those observed in this study have great potential as part of a sustainable, cost-effective agroecological management strategy, which can be integrated with other methods to achieve effective control of FAW.

Influence of Different Herbicides on the Growth, Growth Attributes and Yield of Maize (Zea mays L.) under Central Plains Zone of Uttar Pradesh

The field experiment was carried out at Agriculture Research Farm, Integral University, Lucknow, Uttar Pradesh, India during Kharif, 2021. The experiment was aimed at studying the effect of herbicides on the growth and yield of maize crop. The experiment was laid down in the Randomized Block Design (RBD) with 12 treatments and was replicated three times. The highest plant height was observed in weed free treatment due to weed free environment all over the maize growth period but among the herbicides, treatment(T8) Atrazine @ 0.5 kg a.i. ha-1 PE followed by Tembotrione @ 0.120 kg a.i. ha-1 PoE at 20 DAS showed highest plant height of 115.2 cm, 173.63 cm and 226.29 cm at 40 DAS, 60 DAS and at harvest stage, respectively. The same treatment (T8) also resulted in the highest number of leaves per plant. The highest cob weight per plant, grain weight per cob, number of seed per cob and test weight of 219.23 g, 144.1 g, 579.23 and 27.43 g, respectively was observed in the same treatment (T­8). The highest grain yield (5199 kg ha-1) was observed in the weed free treatment but among the herbicidal treatment, the treatment T8 obtained the highest grain yield of 4803 kg ha-1 and was at par with the treatment (T7) Atrazine @ 0.5 kg a.i. ha-1 fb 2,4-D sodium salt @ 0.5 kg a.i. ha-1 PoE at 30 DAS bearing grain yield of 4723 kg ha-1. Among the herbicidal treatments, the treatment (T8) also resulted in the highest biological yield (12042 kg ha-1) and Harvest index (39.89%) which was at par with the treatment Atrazine @ 0.5 kg a.i. ha-1 PE followed by 2,4-D sodium salt @ 0.8 kg a.i. ha-1 PoE at 30 DAS (T7).

Hydrochar from dairy sludge as phosphorus fertiliser affects greenhouse gas emissions and maize yield

ABSTRACT Dairy processing sludge is a phosphorus (P) rich waste with a high potential to replace mineral phosphorus fertiliser in crop production, with possible enhancement of greenhouse gas emissions to the environment. Hydrothermal carbonisation is a technology that transforms the sludge into a hydrochar. The objective of this study is examining P availability of two hydrochars produced from Danish and Irish dairy sludge and their influence on greenhouse gas emissions and maize yields. The trial assessed (i) Danish dairy sludge; (ii) hydrochar derived from Danish sludge; (iii) hydrochar made from Irish dairy sludge; (iv) mineral phosphorus fertiliser; and (v) control. Emissions of nitrous oxide and carbon dioxide, soil pH, mineral nitrogen contents and crop yields were measured. Treatment with Danish dairy sludge had significantly higher cumulative nitrous oxide emissions while the emissions from both hydrochars were not significantly different compared to mineral phosphorous feriliser. Statistical modelling showed that temperature, soil nitrate content, interactions both between temperature and precipitation, and between soil moisture and precipitation were drivers for nitrous oxide emissions. There was no difference in emissions among all treatments when scaled for yield. Hydrochar may alleviate the enhanced nitrous oxide emissions in soil without constraining P availability and maize crop yields.

Using wavelet transform and hybrid CNN – LSTM models on VOC & ultrasound IoT sensor data for non-visual maize disease detection

Early detection of plant diseases is crucial for safeguarding crop yield, especially in regions vulnerable to food insecurity, such as Sub-Saharan Africa. One of the significant contributors to maize crop yield loss is the Northern Leaf Blight (NLB), which traditionally takes 14–21 days to visually manifest on maize. This study introduces a novel approach for detecting NLB as early as 4–5 days using Internet of Things (IoT) sensors, which can identify the disease before any visual symptoms appear. Utilizing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models, nonvisual measurements of Total Volatile Organic Compounds (VOCs) and ultrasound emissions from maize plants were captured and analyzed. A controlled experiment was conducted on four maize varieties, and the data obtained were used to develop and validate a hybrid CNN-LSTM model for VOC classification and an LSTM model for ultrasound anomaly detection. The hybrid CNN-LSTM model, enhanced with wavelet data preprocessing, achieved an F1 score of 0.96 and an Area under the ROC Curve (AUC) of 1.00. In contrast, the LSTM model exhibited an impressive 99.98% accuracy in identifying anomalies in ultrasound emissions. Our findings underscore the potential of IoT sensors in early disease detection, paving the way for innovative disease prevention strategies in agriculture. Future work will focus on optimizing the models for IoT device deployment, incorporating chatbot technology, and more sensor data will be incorporated for improved accuracy and evaluation of the models in a field environment.

Impact of climate change on potential distribution of Dickeya zeae causal agent of stalk rot of maize in Sialkot district Pakistan

Maize (Zea mays) is an influential crop in its production across the world. However, the invasion of many phytopathogens greatly affects the maize crop yield at various hotspot areas. Of many diseases, bacterial stalk rot of maize caused by Dickeya zeae results in severe yield reduction, thus the need for efficient management is important. Further, to produce epidemiological information for control of disease outbreaks in the hot spot regions of Sialkot District, Punjab Pakistan, extensive field surveys during 2021 showed that out of 266 visited areas, the highest disease incidence ranging from 66.5 to 78.5% while the lowest incidence was ranging from 9 to 20%. The Maxent modeling revealed that among 19 environmental variables, four variables including temperature seasonality (bio-4), mean temperature of the wettest quarter (bio-8), annual precipitation (bio-12), and precipitation of driest month (bio-14) were significantly contributing to disease distribution in current and coming years. The study outcomes revealed that disease spread will likely increase across four tehsils of Sialkot over the years 2050 and 2070. Our findings will be helpful to policymakers and researchers in devising effective disease management strategies against bacterial stalk rot of maize outbreaks in Sialkot, Pakistan.

Effect of limited irrigation and planting systems on yield and water productivity of maize (Zea mays)

A field experiment was conducted during the rainy (kharif) seasons of 2019 and 2020 at the research farm opf ICAR-Indian Agricultural Research Institute, New Delhi to study the effect of limited irrigation application and planting systems on yield and water productivity of maize (Zea mays L.). The experiment was laid out in factorial randomized complete block design with 3 replications. The experiment consisted of two crop establishment methods as one factor, viz. Narrow bed planting and broad bad planting and 5 irrigation levels, viz. 25% DASM (Depletion of available soil moisture); 25% DASM at tasselling, silking, grain filling and 50% during rest of time; 50% DASM at all growth stages; 50% DASM + KCl spray at 40 DAS and at pre-tasselling; and 50% DASM+2% Urea spray at 40 DAS and at pre-tasselling. Experimental results revealed that the broad bed system of planting during 2019 and 2020 recoded significantly higher grain yields (4.37 and 4.57 t/ha) compared to the narrow bed system (4.23 and 4.38 t/ha), respectively. Regarding different irrigation levels, the highest water productivity (91.26 and 199.76 kg/ ha-cm) was observed with irrigation at 50% DASM along with two sprays of urea at 40 DAS during both the years, respectively. It was concluded that broad bed along with irrigation at 50% DASM with two sprays of urea proved to be most effective approach to enhance water productivity and yield of maize crop.

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments.

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

Assessment of microbial exopolysaccharides for improving soil fertility and crop yield in semi-arid India

Soil degradation and poor fertility are major concerns in semi-arid regions of India. Microbial exopolysaccharides (EPS) have demonstrated potential in mitigating abiotic and biotic stresses in these areas. However, the studies on the impact of EPS on crop performance and soil fertility under natural field conditions remain limited. To address this knowledge gap, a field experiment was conducted during the rainy season of 2019-20 and 2020-21 at ICAR-NIASM, Baramati, Maharashtra, to examine the effects of microbial EPS on average crop yields of soybean and maize, microbial colonization status in soybean and maize, and soil fertility parameters in basaltic soils of India. Seeds inoculated with Rhizobium or nitrogen fixers and 10 kg ha-1 EPS resulted in a significant increase in soybean and maize yields by 57.7% and 44.8%, respectively, compared to the control. Additionally, the Rhizobium treated seeds with EPS 10 kg ha-1 showed improvements in soil organic carbon, dehydrogenase activity, and nitrogen content by 15.5%, 214.5%, and 70.0%, respectively, in soybean compared to the control. Conversely, the application of microbial EPS did not cause any significant change in soil pH, E.C., or available P, K, Fe, Mn, and Zn contents in both crops. While the presence of EPS within the rhizosphere of soybean actively promoted nodule formation, it also enhanced microbial colonization within the rhizosphere of both soybean and maize. Moreover, the best subset regression analysis revealed that EPS altered the soil parameters for the yield of both crops. The findings of this study suggest that EPS treatment, in combination with Rhizobium or nitrogen fixers, can significantly enhance crop yield, promote nodule formation in soybean, and improve microbial colonization within the rhizosphere of both soybean and maize. These results highlight the potential of microbial EPS as a viable and sustainable solution to address soil degradation and poor fertility in semi-arid regions of India.

Pengaruh Aplikasi Biochar Tempurung Kelapa dan Pupuk Anorganik terhadap Serapan N P K dan Hasil Tanaman Jagung Manis pada Tanah Gambut

Maize (Zea may ssaccharata) is the horticulture in demand current society this. Limited avalability of land productive agriculture causes agricultural development, especially maize plant using peat soil media. Peat soil are low acidity, and nutrient content macro N, P, K available to plant also low. Attempts made for increasing the productivity of peat soils is by giving biochar and proper fertilization. Provision of biochar and inorganic fertilizer in addition to being a source of nutrients for plants, can also improve the physical, chemical and biological properties of the soil. This study aims to determine the influence of the application of coconut shell biochar and inorganic fertilizers on N P K nutrient uptake and maize crop yields. Research using Complete Random Design (CRD) factorial consists of two factors treatment and three replications. The first factor, namely coconut shell biochar which consist of 3 levels. The second factor, namely inorganic fertilizer consisting of 3 levels so there are 27 experimental units consisting of 2 sets of plants. Based on the results of variance, interaction of coconut shell biochar administration and inorganic fertilizer no significantly influence on all treatments. Whereas the administration of coconut shell biochar has no significant influence on soil pH, K uptake and cob weight, but significantly affected the wet weight, dray weight, N uptake and K uptake. Inorganik fertilizer has no significant influence on soil pH, wet weight, dry weight, N uptake and cob weight, but has a significant influence on P uptake and K uptake. The best cob weight is at A1B3 treatment (biochar coconut shell 55 gram/polybag + inorganic fertilizer 5,1 gram/polybag).

An overview of foliar application of macro and micronutrients on the yield of maize in Ghana

Maize is a major staple crop in Ghana which plays a significant role in consumer diets. For some time now, the farming methods used by the farmers have been negatively affected by components such as climate, soil nutrient depletion, and constant monocropping resulting in the adoption of inorganic fertilizers. Conventional fertilizers supplied through soils are subjected to slow release of nutrients, leaching, fixation, surface runoff, erosion, and volatilization, which hinders optimal plant growth and yield. Foliar application of fertilizer offers an alternative method of fertilizer application that supplies nutrients directly to the stomata and cuticle of the leaves of maize thereby enabling rapid absorption and enhancing crop vigor. The The main objective of this paper is to review research papers which explores the potential of foliar application of vital nutrients – phosphorus (P), sulphur (S), zinc (Zn), and iron (Fe) – to improve the yield of maize crop. According to the literature gathered, foliar fertilization in combination with soil applied fertilizers emerges as a promising strategy, particularly in addressing nutrient deficiencies and stress scenarios. The efficient uptake of nutrients through leaves, as opposed to traditional soil-based approaches, holds promise for augmenting yield and enhancing protein content in maize crops. Notably, prior research highlights the efficacy of foliar-applied P, S, Zn, and Fe in significantly boosting grain yield. By understanding the complex mechanisms of nutrient absorption and the advantages of foliar application, the agricultural sector can explore innovative avenues to surmount soil-related challenges and achieve sustainable crop development.

Eco-physiology of maize crops under combined stresses.

The yield of maize (Zea mays L.) crops depends on their ability to intercept sunlight throughout the growing cycle, transform this energy into biomass and allocate it to the kernels. Abiotic stresses affect these eco-physiological determinants, reducing crop grain yield below the potential of each environment. Here we analyse the impact of combined abiotic stresses, such as water restriction and nitrogen deficiency or water restriction and elevated temperatures. Crop yield depends on the product of kernel yield per plant and the number of plants per unit soil area, but increasing plant population density imposes a crowding stress that reduces yield per plant, even within the range that maximises crop yield per unit soil area. Therefore, we also analyse the impact of abiotic stresses under different plant densities. We show that the magnitude of the detrimental effects of two combined stresses on field-grown plants can be lower, similar or higher than the sum of the individual stresses. These patterns depend on the timing and intensity of each one of the combined stresses and on the effects of one of the stresses on the status of the resource whose limitation causes the other. The analysis of the eco-physiological determinants of crop yield is useful to guide and prioritise the rapidly progressing studies aimed at understanding the molecular mechanisms underlying plant responses to combined stresses.

Effects of Intercropping on Maize and Soybean Yield Performance, Land Equivalent Ratio, and Maize Leaf Area in Conservation Agriculture

Maize-soybean intercropping systems as a conservation farming practice are receiving increased focus from the scientific community. This is because of the advantages of intercropping, especially nutrient benefits through cereal-legume interactions, alternative sustainable methods to manage biotic stress (pests, diseases, weeds), and crop failure risk management due to erratic weather. In addition, smallholders in developing countries commonly use intercropping to produce crops. In Uganda, 40% of smallholder farmers are practicing intercropping yet no adequate location-specific information is available to inform their practice. Farmers who adopted conservation farming practices did not mulch their maize due to drudgery associated with collecting mulch. This study evaluated the effect of two tillage methods (T1 = Conventional tillage using ox drawn mould board plough, T2 = Minimum tillage using ox drawn ripper) and five soil cover practices (SC1 = Mulched Maize, SC2 = Control no mulch, SC3 = Two rows of soybean in between one row of maize, SC4 = One row of soybean in between one row of maize, SC5 = Sole soybean) on maize and soybean yield performance. The trials were established for 4 rainy seasons on a sandy loam ferrosol at National Agricultural Research Organization Institute in Lira, Uganda. Soil cover practice had a significant effect on maize and soybean crop Yield. Mulching significantly increased maize yield and LAI. The LER for both intercropping partterns were above 1.2. Tillage methods were not significantly different in determining crop performance. The practice of minimum tillage should also be adopted because it enhances the positive effects of soil cover (intercropping). We recommend farmers to adopt the intercropping pattern of one row of soybean in between maize row spaced at 75 &amp;times; 30 cm for better LER, and crop performance. This intercropping pattern maximizes on available resources to deliver better output in conservation farming. Maize crop generally performed better during first season as compared to second season. We recommend farmers to utilize the first rains as the main maize production season.

Both class- and landscape-level patterns influence crop yield

Under global climate change, we can adjust landscape patterns to mitigate the effects of climate factors on crop yield. Field and landscape-scale research suggests that increased land cover diversity and non-crop habitats can improve crop yield, but little is known on the effects of patch area and shape of one type of land use (i.e., class-level) and spatial patterns (aggregation, fragmentation, and heterogeneity) of surrounding multiple land-use classes (i.e. landscape-level) on crop yield. We evaluated the relationship between 375 landscape metrics at both class- and landscape-levels, 120 climate variables, and the crop yields of maize, rice, and wheat in China from years 2000–2015 at national and sub-climatic zone scales. Our results suggest that both class- and landscape-level patterns were the main drivers, contributing averagely 12% of the variations in crop yields. Specifically at national scale, crop yield in cropland with forest cover (>27%) was increased by 169% than where without forest cover. Shape complexity in forest and cropland patches, aggregation degree, and spatial complexity of land cover types were more closely associated with crop yield (accounting for 2–73% of the variations in crop yield at sub-climatic zones) than landscape diversity (<2%), mainly owing to their mediating effects on the relationship between climate factors and crop yield. Not all non-crop habitats are beneficial to crop yield, and forest patches with a suitable area and shape complexity perform best. Our findings suggest that the regulation of both class- and landscape-level patterns may be an alternative way to improve crop yield, rather than only enhancing land-cover diversity or increasing the area of non-crop habitats.

Effects of Different Treatments on Seed Germination and Breaking Seed Dormancy in Wild Luffa Species

Comprehending general combining ability (GCA) and specific combining ability (SCA) is pivotal in maize breeding for successful hybrid development. GCA signifies the overall genetic worth of a parent, while SCA demonstrates unique interactions between parents. In instances where GCA variances surpass SCA variances, early assessments become more effective, enabling the anticipation of superior hybrids based on parental GCA impacts. Assessing the combining ability of maize becomes increasingly crucial with escalating temperatures, which can adversely affect maize growth and crop yield. Research conducted in Prayagraj involved utilizing 7x7 half diallel crosses of maize initially developed during the Rabi 2022 season, followed by their subsequent planting in a randomized block design (RBD) during spring 2023. The primary aim of the study was to evaluate the general combining ability (GCA) and specific combining ability (SCA) effects exhibited by the parents and hybrids, respectively, in order to identify superior-performing parents and hybrids among them. The results showcased strong positive GCA in MILCT-145F (P7) and MILC-2050 (P6), while specific heat-tolerant hybrids, such as P7xP1, P6xP2 exhibited outstanding performance in grain yield and other associated traits.

Less negative impacts of climate change on crop yields in West Africa in the new CMIP6 climate simulations ensemble

Food insecurity is among one of the greatest risks posed by climate change in Africa, where 90 to 95% of African food production is rainfed and a large proportion of the population already faces chronic hunger and malnutrition. Although, several studies have found robust evidence of future crop yield losses under climate change scenarios, there is wide variation among crops and regions as well as large modeling uncertainties. A large part of this uncertainty stems from climate projections, as climate models may differ in simulating future changes in precipitation and temperature, which could lead to different future crop production scenarios. This work examines the impacts of climate change on crop yields of maize, millet and sorghum in West Africa using climate change projections from the Coupled Model Intercomparison Project 5th Phase (CMIP5) and from the new generation of climate models from the Coupled Model Intercomparison Project 6th Phase (CMIP6). We use the SIMPLACE crop modeling framework to simulate historical and future crop yields, and bootstrap techniques to evaluate projected changes in crop productivity between the CMIP5 and CMIP6 ensembles. Using the new generation of climate models CMIP6, we find that the negative crop yield projections shown by CMIP5 simulations are largely reduced, with even large increases in crop yields when the effect of atmospheric CO2 concentration is considered in the crop model. These differences in crop yield impacts between the CMIP5 and CMIP6 simulations are mainly due to different climate projections of temperature and precipitation in West Africa; CMIP6 projections being significantly wetter and cooler by mid-century and to a lesser extent by the end of the century. Such results highlight the large uncertainties that remain in assessing the impacts of climate change in the region and the consequent difficulty for end-users to anticipate adaptation strategies.

Boron seed coating combined with seed inoculation with boron tolerant bacteria (Bacillus sp. MN-54) and maize stalk biochar improved growth and productivity of maize (Zea mays L.) on saline soil

Salinity exerts significant negative impacts on growth and productivity of crop plants and numerous management practices are used to improve crop performance under saline environments. Micronutrients, plant growth promoting bacteria and biochar are known to improve crop productivity under stressful environments. Maize (Zea mays L.) is an important cereal crop and its productivity is adversely impacted by salinity. Although boron (B) application, seed inoculation with boron-tolerant bacteria (BTB) and biochar are known to improve maize growth under stressful environments, there is less information on their combined impact in enhancing maize productivity on saline soils. This study investigated the impact of B seed coating combined with seed inoculation with BTB + biochar on maize productivity under saline soil. Four B seed coating levels [0.0 (no seed coating), 1.0, 1.5, 2.0 g B kg−1 seed], and individual or combined application of 5 % (w/w) maize stalk biochar, and seed inoculation with Bacillus sp. MN-54 BTB were included in the study. Different growth and yield attributes and grain quality were significantly improved by seed coating with 1.5 B kg−1 seed coupled with biochar + BTB. Seed coating with 1.5 B kg−1 seed combined with biochar + BTB improved stomatal conductance by 32 %, photosynthetic rate by 15 %, and transpiration ratio by 52 % compared to seed coating (0 B kg−1 seed) combined with biochar only. Similarly, the highest plant height (189 cm), number of grain rows cob−1 (15.5), grain yield (54.9 g plant−1), biological yield (95.5 g plant−1), and harvest index (57.6 %) were noted for B seed coating (1.5 g B kg−1 seed) combined with biochar + BTB inoculation. The same treatment resulted in the highest grain protein and B contents. It is concluded that B seed coating at 1.5 g B kg−1 seed combined with biochar + BTB inoculation could significantly improve yield and quality of maize crop on saline soils. However, further field experiments investigating the underlying mechanisms are needed to reach concrete conclusions and large-scale recommendations.

Co-implementation of tillage and precision nutrient management practices on crop yield of kharif maize (Zea mays)

Our accustomed agricultural practices of tilling the soil continuously and excessive fertilizer applications caused not only a decrease in organic carbon content and soil degradation but also disturbed environmental harmony. With this backdrop, current study was carried out during rainy (kharif) seasons of 2018 and 2019 at the research farm of Trihut College of Agriculture, Dholi, Pusa, Bihar to evaluate the effect of different tillage and nutrient management practices on maize (Zea mays L.). The experiment was laid out in split-plot design with tillage as the main plot and nutrient management as the sub-plot. The results showed significantly superior maize grain yield in PB (6.1, 6.2 mg/ ha) and 60% RDN+GSGN (6.0, 5.9 mg/ha) over CT (4.9, 5.0 mg/ha) and RDF (5.1, 5.4 mg/ha) during 2018 and 2019. Similar trend was followed in stover yield, net returns and B:C ratio. Among path analysis, harvest index and no of grains/cob observed largest direct path coefficient of yield attributes to determine the maize grain yield and stover yield respectively. The results suggest that the adoption of conservation tillage and precise nutrient management practices will help in the realization of better yield with maximum profit by way of reducing the input capital apart from improving soil health thereby sustaining natural resources for future generations.

Design and experiment of maize kernel loss monitoring system based on signal interval duration

Maize is one of the world’s major food crops, in the process of mechanized maize harvesting, the irrational setting of the harvester’s working parameters will cause excessive kernel loss, which will have a bad impact on the quality and quantity of the maize crop yield. Real-time monitoring of loss during harvesting is the basis for dynamic adjustment of working parameters. In order to obtain the amount of kernel loss during the harvesting process and provide real-time feedback to the operator on the current kernel loss data, so that can adjust the working parameters in time to reduce the subsequent yield loss, this study proposed a real-time kernel loss monitoring system applicable to maize crops. Aiming at the limitations of the traditional monitoring system in terms of accuracy, the signal processing circuit of the monitoring sensor was improved and a loss kernel counting algorithm based on signal interval duration was proposed, which corrected the monitoring error of the traditional measurement method in the environment of high-frequency material impact. Finally, the performance of the designed monitoring system was verified under different working environments, and the results show that the monitoring system has good performance and the monitoring accuracy can reach more than 92% under different kernel flow rates and different sensor installation positions. In addition, the monitoring system has high adaptability when it comes to kernels with different moisture contents, and the accuracy of identifying lost kernels in mixtures can reach 95%. Compared with the traditional monitoring system, the system proposed in this study has higher monitoring accuracy.The research results of this paper are of great significance to the intelligent control system of maize combine harvester, and provide technical support for real-time regulation of working parameters based on kernel loss, so as to reduce grain loss and ensure grain yield safety.