Effect of tilth index and spot tillage with Robotic spot tiller cum planter on maize crop growth parameters

Introduction

Maize (Zea mays) is one of the most crucial cereal crops globally, serving as a staple food for millions of people. After rice and wheat, maize is the third-most significant food crop in the Indian scenario (Murdia et al., 2016) [2] . One of the most resilient developing crops, maize (Zea mays L.) has a larger range of adaptation under a variety of agroclimatic situations. Due to its highest genetic production potential among all cereals, maize is referred to as the "queen of cereals" worldwide. It accounts for 39% of the world's grain production and is grown over approximately 190 million hectares across 165 nations with a greater diversity of soil, climate, biodiversity, and management techniques (APEDA, 2020). Excessive soil compaction is considered as the prime reason for the physical degradation of agricultural soils (Nawaz et al., 2013) [3] . The soil compaction was induced wheel load of agricultural machinery, tire dimensions, tire-soil contact characteristics and number of wheel passes (Obour and Ugarte 2021 and Gao et al., 2007) [4,5] . Severe soil compaction hindered substantial nutrition and moisture absorption, reducing root development and distribution (Bengough et al., 2011 and Schjønning et al., 2016) [6,7] . The yield penalties caused by compaction in maize fields have been documented to reach 43% (Voorhees, 2000) [8] . The way that various crops react to compacted soil varies greatly. The emergence of maize, barley, and soybean seedlings is more impacted by soil compaction. According to a recent study, the compaction of agricultural tractor tyre systems decreased corn height in the first two years, while for soybeans, this effect became considerable in the third year (Shaheb, 2020) [25] . According to studies, soil compaction increases the soil's mechanical strength and decreases its porosity, which have the greatest effects on the root penetration capability (Kirby & Bengough 2002; Kristoffersen and Riley 2005) [26,27] . Tillage is the mechanical manipulation of soil to create a favorable environment for seed germination. Tillage is one of the most important methods to reduce the soil compaction (Daniells, 2012) [14] . Soil physical properties were greatly influenced by tillage practices (Mosaddeghi et al., 2009) [17] . Crop yield, root distribution, plant population density, and seedling emergence were all impacted by changes in the physical properties of the soil (Khurshid et al., 2006) [24] . Different tillage practices create different soil conditions which may affect the growth and yield of maize. Deep tillage and continuous tillage over a period of time leads to soil erosion, soil degradation, soil structure gets diminished, reducing soil productivity and soil organic carbon and also it consumes lot of fossil fuels adding up to greenhouse gas emissions. Traditional maize planting methods in developing countries rely on broadcast sowing, which is cost-effective but results in higher seed costs, increased intercultural operational expenses, and lower grain yields. (Swain et al., 2020) [20] . Although zero or no tillage had given good results in conserving soil moisture and other resources in field but long-term effects may lead to increase in bulk density which is not conducive to maize growth ultimately ending up in reduced yields. In recent years, the use of robots in agriculture has gained popularity due to its potential to increase efficiency and reduce labour costs (Matin et al., 2008; Krishna, 2017; Harmanda et al., 2019) [16,23,9] . Robotic planting technology holds immense promise for the agricultural industry, offering a range of substantial benefits. To address the problems of deep tillage and zero tillage, a minimum tillage concept called robotic spot tillage was introduced in the present study. Robotic spot tillage is one type of tillage practice where a specific and minimum area which influences the effective root zone of maize plant is disturbed to a particular depth and width for seed placement. This tillage practice ensures resource conservation, avoids soil erosion and also reduces draining of nutrients from soil. There were only few research papers which have mentioned this topic, so a study is needed on robotic spot tillage. The present study is aimed to understand the effect of robotic spot tillage on maize growth parameters.

Material and methods

Site description

A study was conducted at the testing field of Division of Agricultural Engineering, ICAR-IARI, New Delhi. The maize variety chosen for the study was Pusa HQPM-1. The soil of the experimental field was sandy loam. In the current study, a designated experimental field measuring 108 m2, with dimensions of 6m x 18m (width-length), was selected. Plant spacing 60*20 cm. The Robotic spot tiller cum planter (fig. 1) was developed in Division of Agricultural Engineering, ICAR-IARI, New Delhi with overall weight of kgs, dimensions of 1.4 X1.4 m. Key components such as stepper motors, drivers, CNC controllers, and electronic components were carefully selected to ensure precision and reliability. The integration of a robust power source, consisting of six 12V batteries, was determined based on current requirements and desired runtime. The communication interface, featuring the FS-TH9X 2.4GHz 9CH transmitter and FS-IA10B receiver, played a crucial role in providing real-time control over the robotic platform's movements.

Stepper motors, including Nema 23 and Nema 17, paired with DMA 860H and TB6600 drivers, facilitated precise control over spatial movements and seed metering. The DDCS V3.1 CNC controller served as the central nervous system, orchestrating the coordination of various components for spot tilling and planting tasks. The Sabertooth dual 25A motor driver ensured efficient control over high-powered motors, contributing to the versatility of the robotic system. The E-bike MY1016ZL 24V 250W DC Motor and BLDC motor with controller provided reliable propulsion and precise control for the rotary tillage tool. The experimental design selected for the robotic sowing with suitable tillage combinations was randomised complete block design with SAS software. The experimental design outline for manually sowing maize in the field was given in Table 1. These treatments combinations included variations in seed placing zones, seed placement depth, and consecutive tillage activities for robotic sowing, as detailed in Table 2. The three levels of diameter selected were 5, 10 and 15 cm, depth selected were 5, 10 and 15 cm and tillage types with speed of operation selected were 150 rpm, 200 rpm and 250 rpm were involved in the experiment respectively. Agronomical practices like nutrient application, irrigation, weed removal were precisely carried out to ensure proper growth of maize plants. The critical soil parameters for the measurement of tilth index of the spot tillage zone in manual sowing methodology were bulk density (BD), cone index (CI), aggregate uniformity coefficient (AUC), soil organic matter (SOM), and plasticity index (PI).

Sample collection Bulk density

The bulk density was measured by core sampler method as delineated by Arshad et al. (1997). The specific volume of core was inserted into the soil, removed slowly without the loss of any soil contents and then it was weighed. This soil sample subsequently dried in oven for 1050C and its weight was measured. It was calculated by equation 1:

Cone Index (CI)

The cone index was measured by cone penetrometer. The process involved placing the cone (3 mm base diameter and an apex angle of 60 degrees) on the soil surface with the shaft held vertically, and the cone's base securely positioned on the soil (Perumpral, 1987). The determination of soil cone index utilized the expression (Equation 2) recommended by Macmillan (2002), assessing the resistance encountered by the cone during penetration.

Aggregate uniformity coefficient (AUC)

Sieve analysis is fundamental technique used for determining aggregate uniformity coefficient (AUC). It involves standard set of sieves (No. 4 sieve (4.75 mm) to the No. 200 sieve (0.075 mm) were shaked thoroughly with progressively smaller square openings. For interpretation convenience, parameters like D10, D30, and D60, corresponding to grain diameter (in mm) at 10%, 30%, and 60%, respectively, were utilized. Additionally, the coefficient of uniformity (Cu), acting as a shape parameter, was computed according to the method outlined by Kaliakin (2017) using Equation 3.

Plasticity Index (PI): For the present study Liquid limit and Plastic Limit were calculated to obtain the Plasticity index (PI). The plastic limit (PL) was determined by rolling out a thread of the fine soil portion on a flat, non-porous surface, following ASTM Standard D 4318. The liquid limit was found out by Casagrande's apparatus. The test follows ASTM standard, allowing interpolation for the moisture content requiring 25 blows or using a correction factor for liquid limit determination from a single moisture content test. Plasticity index was calculated by Equation5 (Kaliakin, 2017)

The soil descriptions based on PI categorize the non-plastic soils with a PI of 0, slightly plastic soils with PI less than 7, medium plastic soils with PI between 7-17, and highly plastic soils with a PI exceeding 17. The soils with a high PI predominantly consist of clay, while those with a lower PI tend to be silt-dominated. Soils with a PI nearing zero typically lack significant clay or silt content, indicating minimal fines.

Soil organic matter:

In our present study, soil organic carbon (OC) was determined using the Walkley-Black (WB) method established by Walkley and Black in 1934. This method involved oxidizing carbon with acidic dichromate (Cr2O72-) followed by titration with ferrous sulphate to determine the amount of unreacted dichromate (Plate 3.11). The OC was then calculated from the difference between the total dichromate added and the unreacted amount. This methodology (Mylavarapu et al., 2014) provides an estimate of soil organic matter (OM) based on OC determination. The calculation of OC, followed the Equation 6.

Where, mLB is the Blank value and mLS is for soil sample Tilth index (TI) Calculations: In this study, a scientific approach was employed to develop a "tilth index" by integrating five key soil physical properties: bulk density, cone index, aggregate uniformity coefficient, organic matter content, and plasticity index. The resulting index, ranging from zero (indicating conditions unusable for plants) to one (representing a nonlimiting environment for plant growth), offers a comprehensive assessment of soil tilth. (Karlen et al., 1990). Hence, the tilth index is a vital indicator of soil quality and structure and it was calculated systematically using the all gathered soil parameters mentioned above. The derived tilth index values provided valuable insights into soil tilth and health as provided in the Equation 7.

Where, CFx is the tilth coefficient for the soil property (X), and A0, A1, A2 are constants. The tilth index (TI), was estimated as the multiplicative combination of the tilth coefficients (Equation 8), and tilth coefficients were normalized to range between 0 and 1. This normalization technique was considered based on the productivity index developed by Singh (1992). Table 3 provides the insights into the limiting, non-limiting and critical values of tilth coefficient (Singh, 1992).

TI = CF(BD) × CF(CI) × CF(AUC) × CF(SOM) × CF(PI)… (8)

Where, TI is the tilth index, CFx is tilth coefficient for soil property (X).

Plant height

The measurement of PH, a crucial indicator of growth, was conducted using a precise measuring tape or metric rulersto accurately determine the vertical distance from the plant base to the apex of the tallest shoot.

Leaf area index:

The leaf area was precisely quantified using a LI-3100C area meter. This equipment accurately measured the leaf area in square centimeters (cm²).

Root parameters:

The root samples were systematically collected from crop rows at 30, 60, and 90-day intervals during their growth period using a root auger. Root parameters such as root length and root volume were measured. This involved scanning the washed roots using a LA2400 scanner (Fig 2a) 4. Importantly, the tilth index, a key parameter reflecting overall soil quality, demonstrated variations among treatments. Specifically, Treatment T1 consistently displayed the lowest tilth index of 0.38, while T3 consistently showed the highest tilth index of 0.69. During the 30, 60, and 90-day plant growth period, a thorough investigation was conducted to assess the individual and interaction effects of key parameters, namely planting depth and tilled zone diameter. The results indicated significant differences in these effects, highlighting the distinct impact of each factor on plant growth. The high R-square value of 0.946, 0.976, and 0.975887 at 30, 60, and 90 days respectively underscores the reliability of the model in explaining the variation in plant height based on the specified parameters the results obtained, robotic tilling and sowing yielded the calculated soil parameters: bulk density ranged from 1.31 to 1.67 g/cm³, ³, cone index range of 1.19 to 2.85 MPa, aggregate uniformity coefficient varied from 2.18 and 4.66 soil organic matter content ranged 0.59% to 0.70% and plasticity index values ranged from 0.21 to 0.37. Robotic tilling and sowing the treatment T1 consistently displayed the lowest tilth index of 0.38, while T3consistently showed the highest tilth index of 0.69. Across the 10*15*T3 and 15*15*T3 treatment combinations, all crop parameters demonstrated higher values, indicating the overall effectiveness of the robotic spot tilling instead of tilling the whole field for normal growth of maize crop. Thus, robotic spot tillage is an effective and efficient alternated method of site-specific tillage in which highest tilth index is obtained along with normal growth of maize crop. The integration of scientific research findings with the implementation of the robotic spot tiller cum planter represents a cutting-edge approach to modern agriculture, resonating with the global imperative for sustainable and technology-driven farming practices.

Acknowledgement

AcknowledgementThe authors are sincerely thankful to division of agricultural engineering,ICAR-IARI, New Delhi for providing site and necessary instruments to carry out the research.

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