Effects of Soil and Foliar Applied Fertilizers on Growth, Yield and Sugar Quality of Two Sugarcane Cultivars under Rainfed Conditions

Background: In north-eastern Thailand, sugarcane is planted normally in late rainy season wherein the plants may experience drought stress during its early growth stage in dry season and waterlogging stress during late growth stage at peak of rainy season. Hence, the objective of the present study was to investigate the effects of soil application alone and soil combined with foliar application of nutrients on growth, yield and sugar quality of sugarcane grown under rainfed conditions. Methods: The field experiment was conducted during November 2016 to December 2017. A split-plot design with three replications was laid out. The two sugarcane cultivars (KK3, K93-219) were assigned as main plots. The fertilizer application methods were assigned as sub-plots that comprised of four treatments: (1) soil applied NPK, (2) soil NPK + foliar N and K applied at 90 days after planting (DAP), (3) soil NPK + foliar N and K applied at 210 DAP and (4) soil NPK + foliar N and K applied at 90 and 210 DAP. Result: The soil NPK + foliar N and K applied at 90 and 210 DAP improved yield components and cane yield. The cultivar K93-219 produced significantly higher cane yield than KK3. The fertilizer application methods and cultivars had no significant effect on sugar quality such as brix (%), purity (%), polarity (%), fiber (%) and commercial cane sugar (CCS-%).

Effect of main cultivation methods in dry rainfed conditions on the amount of weed under winter wheat

Relevance. Against the background of predecessors, soil cultivation and nutritional conditions, weeding of the area under Barakatli 95 durum wheat variety was studied and the results obtained are given in the article. Thus, high weeds in the field reduce the quantity and quality of crops. One of the main factors preventing this is the application of proper cultivation methods appropriate to the region. This is one of the most important and urgent issues in agriculture.Methodology. The research was conducted in a 3-factor (2×3×3) field experiment in a short-rotation crop rotation (pea-wheat-wheat) located at the Jalilabad Regional Experimental Station in the rainfed conditions of South Mugan. The amount of weeds in the field was studied in the first decade of March and April by counting weeds per 1 m2 from different parts of the field according to the options.Results. The results of the 3-year study (average for 2019–2021) showed that the effect of predecessors, soil cultivation and nutritional conditions on the amount of weeds under winter wheat was different. Among the cultivation options, relatively high weeding soil was obtained with a heavy disc harrow at a depth of 10–12 cm in the form of 1 disc, and against the background of feeding conditions, N60P60 + 10 tons of manure on all three cultivations, and the least weeding was observed in the variant where N90P60K45 fertilizer norm was applied in 2 discs at a depth of 10–12 cm with a heavy disk trowel after the pea predecessor.

Integrated Nutrient Management Improves the Growth and Yield of Rice and Greengram in a Rice—Greengram Cropping System under the Coastal Plain Agro-Climatic Condition

Continuous mono-cropping of rice has resulted in decline or stagnation of yield output due to the occurrence of multiple nutrient deficiencies and worsening of soil physicochemical properties accompanying increased pressure of insect pests and diseases. The basic concept of integrated nutrient management (INM) is maintenance or adjustment of soil fertility and supply of plant nutrients to an optimum level for sustaining the desired crop productivity through optimisation of benefits from all possible sources of plant nutrients in an integrated way. Augmenting a rice-based cropping system with pulses is a prevalent and indigenous cropping system under rainfed conditions. Considering the above facts, experiments were conducted to evaluate the impacts of integrated nutrient management on productivity of aromatic rice–greengram cropping system and nutrient balance of the post-harvest soil for agricultural sustainability under rainfed conditions in two consecutive years (2017–2018 and 2018–2019) with six main plots and three subplots. The experimental findings revealed that the treatment comprised of 50% recommended dose of fertiliser (RDF) through chemicals + 50% recommended dose of nitrogen (RDN) through farmyard manure (FYM) increased the plant height, tillers, dry matter accumulation, leaf area and leaf area duration, and yield parameters in short grain aromatic rice. Similarly, preceding application of 50% RDF + 50% RDN through FYM to rice and further application 75% RDF + Rhizobium+ phosphate solubilizing bacteria (PSB) to greengram increased the growth characteristics and yield parameters—such as pods/plant, seeds/pod, grain yield, stover yield, and harvest index—in greengram. It was concluded that the treatment consisting of 50% RDF (chemical fertiliser) + 50% RDN (FYM) to rice and 75% RDF + Rhizobium + PSB to greengram increased the productivity of the rice–greengram cropping system. Furthermore, the adoption of INM has positively impacted post-harvest soil nutrient balance.

Tillage effect on agronomic efficiency of nitrogen under rainfed conditions of Tanzania.

Nitrogen (N) deficiency is a common feature in soils managed by smallholder farmers in Africa. Crop residue retention, in combination with no-till (NT), may be a pathway to improve agronomic use efficiency of applied N for small-scale farmers under the predominant rainfed conditions. This chapter reports on the results of a study carried out over two cropping seasons in the long rains of 2014 and 2015 on two sites: (i) on-farm (Mandela); and (ii) a research station (SARI) in eastern Tanzania. The experiment consisted of two tillage systems, conventional tillage (CT) and Conservation Agriculture (CA), with a minimum of 2.5 t ha-1 crop residue cover maintained in the plots during the experiment. CT consisted of soil inversion through tillage and removal of crop residues. In the on-farm experiment, maize was grown in plots with four rates of N application: 0, 27, 54 and 108 kg N ha-1. In the on-station trial, five rates were used: 0, 20, 40, 60 and 100 kg N ha-1. Maize yield and agronomic efficiency (AE) of N were used to assess and compare the productivity of the tested treatments. The results showed that tillage, soil type and rate of N application influenced crop productivity. In the clay soils, the differences between tillage practices were small. Under CT, AE ranged between 21.6 and 53.9 kg/kg N, and it was 20.4-60.6 kg/kg N under CA. The lowest fertilizer application rate of 27 kg ha-1 often had the largest AE across the soil types and tillage practices. In the on-station trials at SARI, the largest AE of 24.6 kg/kg N was recorded under CA with 40 kg N ha-1. As in the on-farm trials, the highest N application rate on-station did not lead to the largest AE. In the CT, AE ranged between 11.5 and 16.8 kg/kg N compared with a range of 15.1 to 24.6 kg/kg N for the CA treatment. Overall, crop residue retention, in combination with NT, is important to improve soil moisture and use efficiency of applied nutrients. Additionally, the initial soil fertility status is also important in determining the magnitude of short-term crop response to applied nutrients. Innovative pathways are needed to achieve the multiple objectives played by maize crop residues for results reported here to be sustainable. However, efficiency of nutrient use needs to be assessed, together with returns on investments, as small yields may mean high nutrient use efficiency but not necessarily significant increased returns at the farm level.

Evaluation of Achillea, Matricaria, and Anthemis plants following selection for drought tolerance at seedling stages

The genetic potentials of eight species of Achillea (A. millefolium, A. fillipendulla, A. biebersteinii, A. nobilis, A. eriophora), Matricaria (M. ricotita), and Anthemis (An. haussknechtii and An. tinctoria) under drought conditions during the seedling stage were measured. Non-ionic water-soluble polymer polyethylene glycol (PEG, molecular weight 6000) was used to simulate water stress at five osmotic potential levels (0, –0.3, –0.6, –0.9, and –1.2 MPa). An acceptable threshold value for germination was osmotic potential –0.6 MPa, and the modest osmotic potential was –1.2 MPa for studied taxa. Seedlings of germinated at two control and osmotic potential –0.6 MPa (as an acceptable threshold value for germination) treatments were sowed in a field under rainfed conditions. Genetic differentiation of control plants (CP) versus early selected plants (ESP, germinated at osmotic potential –0.6 MPa) was studied using morphological, physiological, and molecular (ISSR) markers. No significant differences were observed between morphological traits of CP and ESP in all species, however, days to full flowering shortened in ESP. The physiological results demonstrate that under rainfed conditions, the ESP, in a quick response, collect osmolytes and amplify the activity of antioxidative enzymes to survive drought. The genetic relationship in the group of genotypes, that ISSR marker set it out, is affiliated to taxon even though AMOVA showed a partial differentiation between CP and ESP groups (21%). It was concluded that the selection of tolerating individuals at the seedling stage represents a likely positive strategy to have higher drought tolerance feature in plants under rainfed conditions.

Markov chain model probability of dry, wet of rainy wet weeks during moonson period over Andhra Pradesh

For agricultural planning, it is important to know the sequence of dry, wet periods. For this purpose a week period may be taken as the optimum length of time. The success or failure of crops particularly under rainfed conditions is closely linked with the rainfall patterns. In this study the Markov chain model method has been applied to know the probability of having a dry or a wet week and consecutive dry or wet periods of 2 or 3 weeks during monsoon period over Andhra Pradesh.

Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks

Using UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R2 > 0.40 than in rainfed areas, which had a maximum R2 value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R2 in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R2 = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.