Modeling Growth and Yield of Crops Using Different Tillage Systems

Abstract

Tillage, an integral component of crop production systems, affects crops growth and yield. Different experiments conducted by researchers on diverse tillage systems, and proposal of different models for predicting crops output were presented. Estimating plant yield has positive value for sustainable development. The number of factors determining crop productivity makes modeling crop production challenging task. Forecasting crop production is challenging, requiring making inferences on future performance based on past conditions. Farmers focus on yield as cropping commences. Formerly, yield prediction was calculated by analyzing farmer\'s previous experience on particular crop. Finite model equations are used to predict output of crops during cultivation provided soil physical properties and their growth parameters are revealed. Different tillage systems including conventional, conservative, reduced, zero, mulch, ridge, minimum and strip in predicting crops growth and yield were discussed. Notable models for predicting growth and yield of crops using different tillage systems were highlighted. Yield of crops is estimated during cultivation provided soil physical properties, climate conditions, and growth parameters of crops are known. Modern farming, utilizing model equations for estimating output, using soil physical properties, climate conditions and relevant parameters of tillage systems are recommended for improvement in crop production depending on soil types.

1. Introduction

Tillage is part and parcel of crop production systems since inception of agricultural practice. The process of soil preparation was greatly refined with the invention of the first plow and since then, various types of tillage equipment and modalities for preparation and cultivation of seedbed have been designed and implemented. Development and application of modern equipment for tillage practices were introduced in the last one decade throughout the world, giving way to the essence of precision manipulation of soil [1]. Modern tillage systems make efforts to minimize the quantity of tillage operations, the amount of energy used in terms of human, materials and machineries [2], the quantity of dust that is created [3] and the bulk of soil disturbance [4].

Tillage practices greatly affect crop growth and yield [5]. It is one of the fundamental agrotechnical operations in agriculture as recorded by [4, 6, 7, 8, 9, 10]. It is a known fact that passes of tractor wheels during tillage result in stiffness of the soil that is capable of affecting both the soil properties and subsequent effects on crop development to maturity and fruiting. Odey et al. [11] and Odey [12] revealed that soil compaction has adverse effects on crop production, affecting agricultural produce in the entire globe.

A number of researchers have studied how soil manipulation affects agricultural output generally. Adekiya et al. [13] reiterated that tillage is important for sustainable okra production on arable soil of southwest Nigeria. According to Ozpinar and Isik [14], tillage systems affecting soil properties are conventional, conservative, reduced, zero or direct seeding, mulch, ridge, stale seedbed, minimum and strip [1, 15, 16]. The state of the earth for agricultural practice should have enough water and plant food, including air to enhance development of roots through it [17, 18, 19, 20, 21, 22, 23, 24, 25].

Nowadays, concentration of tillage practices has been much on soil conservation as well as on zero tillage method, primarily to limit cone index and erosion of soil, reduce drought and cost of soil manipulation, enhancing biological activities in the soil for increased crop nutrients for abundant crop production, limiting overall carbon-dioxide release, taming the consequences and depletion of the ozone layer [11, 12, 26]. Generally, okra production is influenced by physiochemical conditions of the lithosphere, including the load-bearing capacity of the soil, density, structure, texture, colloids, nitrogen (N), pH, phosphorus (P), carbon (C) and many more.

The aim of this chapter is to present different field experiments conducted by researchers on different tillage systems and also to propose different models for predicting growth and yield of crops, for enhancement of agricultural production.

2. Tillage systems

Tillage is the physical manipulation of soil using tillage equipment to cut, lift, turn, pulverize and break soil clogs in order to prepare the soil for planting. Tillage is broadly classified into primary and secondary. Primary tillage is the initial manipulation done on the soil using primary tillage equipment such as plows to break the soil. While secondary tillage operations are subsequent manipulations done on the soil to further prepare the soil for planting. Depending on the type of seedbed required by the farmer, the type of crops to be planted, soil condition, cost of tillage and the availability of equipment, farmers can decide to adapt different types of tillage systems, as explained in the following paragraph. Different researchers/authors have revealed their thoughts on different tillage practices, notable among them are Rasmussen [19]; MWPS (MidWest Plan Service) [20]; Owen [21]; ASAE (American Society of Agriculture and Biological Engineers) [22]; Leonard et al. [23]; Hedayatipoor and Alamooti [24] and Odey [25].

2.1 Conventional tillage

In conventional tillage, operation plowing and harrowing are carried out more than once before planting of crops. This system of tillage has been historically and traditionally used to prepare a seedbed and produce a given crop [20, 22]. Thus, in conventional tillage practice, less than 30% of the soil surface is incorporated by crop remains after cultivation.

2.2 Conservative tillage

Conservative tillage is a tillage practice that incorporates at least 30% of the crop residues after preparation and adopted mainly to reduce water erosion among other benefits. Thus, about 1120 kg/ha is mixed with the soil after tillage [21]. Currently, there is a redirection toward shifting to the conservation and no-tillage methods purposely to reduce soil compaction, control erosion, conserve soil and water in the soil, mitigate drought, reduce cost of tillage, increase soil organic matter, boost crop productivity, and to reduce net CO₂ emissions which contribute to global warming [11, 12, 25, 26].

2.3 Reduced tillage

This type of operation allows minimum number of equipment passes along the cultivated area than the conventional tillage. This system employed in agricultural practice enables at least 15% but less than 30% coverage by surface residue after cropping.

2.4 Zero tillage

In this system, the cultivated land is not manipulated throughout the cropping season. However, soil disturbance could result only during fertilizer incorporation and planting using seed drills, openers and coulters.

2.5 Mulch-tillage

Mulch-tillage refers to a practice of leaving 30% and above of crop remains on the top soil to enhance moisture on the soil, protect and reduce excess heat in the soil and generally maintain optimum soil condition. Different tillage equipment and tools are utilized during mulch-tillage, thereby enhancing and maintaining adequate soil water throughout the cropping season [22].

2.6 Ridge-tillage

In this type of system, the agricultural land is not manipulated throughout the season. Thus, fertilizer and seeds are applied on beds and ridges previously made in the last planting season using specialized equipment and tools, such as sweeps, disks and furrow openers. Weeding operation is carried out by the use of herbicides.

2.7 Stale seedbed

Stale seedbed production systems make use of full-width tillage after harvest, sometimes using tillage equipment comparable to the ones utilized during mulch-tillage operation. In this system, ridges or beds are not manipulated but allowed to stale till the next cropping season. Contact herbicides are utilized in weeding. Preparation of land can take place in this system of tillage compared to mulch-tillage system. Thus, no further manipulation is done after the land preparation.

2.8 Minimum tillage

Minimum tillage has been used by the CT Workgroup [4]. It refers to a practice that reduces tillage passes, leading to conservation of fuel for a given crop by at least 40%. The minimum tillage system aimed at meeting up with the 40% and above decrease in the soil disturbance. As reiterated by the researchers in the University of California, 50% of fuel and 72% of time can be saved when utilizing the machine called “Incorpramaster” that operates on only one strip, in contrast to disk plowing and harrowing [2].

2.9 Strip system

In the strip system of tillage practice, tilling is done on the row where crops are to be planted. This is to make sure that crop remains are removed before it is tilled before planting to ensure residue removal, subsoiler usage, and most times allowing for crop land warming and drying.

2.10 Precision tillage

In this type of tillage system, tillage is carried out exactly at the point(s) where it is required. Cost of tillage is greatly reduced since the number of tractor passes is limited; thereby fuel consumption is reduced.

3. Growth and yield of crops

The load-bearing capacity of the soil, density, structure, texture, colloids, N, pH, P, C and many more have effects on agricultural production and output [27]. Different factors responsible for agricultural production output and hazards associated with cropping are available nutrients, including water in the soil, environmental condition, pests and plant diseases. Agriculturists must monitor and control these factors to enable bountiful harvest.

3.1 Factors that affect crop growth and yield

Five important factors affect crop growth and yield are (i) soil fertility, (ii) soil water availability, (iii) climate conditions, (iv) pests and (v) diseases. Out of these, the availability of water in the soil is affected by the type and efficiency of tillage practice adopted. Tillage systems affect soil structure, compaction and water availability for nutrients’ utilization. Eighteen essential nutrients contribute to crops’ well-being. Their availability in the soil provides assurance for effective development of crops as these essential nutrients are needed in varying quantities, and hence are categorized into macro- and micro-mineral components. The elements in the soil provide essential nutrients toward the growth and yield of crops in terms of development of parts such as the roots, stems, leaves and fruits. These essential nutrients enhance production of hormones and proteins vis-à-vis manufacture of chlorophyll during photosynthesis. Limited supply or lack of any of the elements leads to decrease in crop growth and overall yield.

Thus, the presence of water in the soil has immediate and direct effect on plant growth and yield. Climate and weather conditions have a direct impact on water availability, with scanty or no precipitation leading to drought, causing death of crops, while excess precipitation leads to too much water, which has a negative impact on crops.

Other factors that cause devastating effects on plant growth and yield are the presence of diseases and pests on the crop land. Factors such as the presence of pests and diseases affect crop growth and yield. They are available in different sizes and shapes causing havoc in different ways to crops at varying degrees. Some nematodes also cause damages to plant roots, thereby truncating nutrients’ flow abilities of such roots, which then affect the overall growth and yield of the crops.

3.2 Effect of soil compaction on crop growth and yield

Compaction of soil results due to densification of units of soils, thereby contributing to the decrease in their voids. Soane and Ouwerkerk [28] established that compaction leads to compression of the soil particles, leading to an increase in component weight, with considerable volume of air reduction. Soil compression results when the structure can no longer withstand external pressure, leading to a disintegration of soil structural units, soil volume reduction, increase in bulk density, decrease in porosity and soil hydraulic conductivity. As reported by Manor and Clark [29]; Petersen et al. [30]; Wells et al. [31]; and Alameda and Villar [32], soil compaction limits crop growth and yield, by restricting root development and movement of air and water within the soil. Soil compaction in the surface layer can encourage runoff and soil erosion, thus increasing soil and water losses.

Mason et al. [33] reported that the capacity of plant roots to pierce the soil is limited by increase in the load-bearing capacity of the soil terminating completely on 2.5 mega Pascal mark. Aase et al. [34] reiterated that as the penetrometer reading gradually moves toward the 2.0 mega Pascal mark and passes the said limit, the development of crop roots would have revealed retarded values. Therefore, 2.0 MPa has been looked upon as a process of determining hard pan layer of soil Wells et al. [31]. Raper et al. [35] further contributed that the life-threatening limit of resistance to penetration, limiting root development, is between 40 and 50 cm deep into the soil. Thus, subsoiler may be used to penetrate the soil for an easy root growth. Taylor [36], Monroe and Kladivko [37], Mason et al. [33], and Mari and Changying [38] elucidated that hydrostatic force in the growing section of the plant root offers the vigor required to drive the root top and meristematic region along the counterattacking soil. Thus, as long as the turgor is not enough to overwhelm fence battle and resistance of the soil, the growth of that exact root cap stops. Crops cultivated in compacted soils have revealed a smaller number of lateral roots than plants grown under controlled condition. Soil compaction has disadvantages on plants by—elevating the force against the growth of crop roots; interfering with the area of the voids and exasperating plant root ailments [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]. Al-Adawi and Reeder [54] reported that subsoiling generally improves growth and yield of all treatments, including control research.

3.3 Measurement of growth and yield parameters in plants

Different researchers have revealed that the growth and yield parameters of crops are measured by gathering data on a regular basis [40, 41, 43, 44, 45, 47, 49, 50, 55, 56, 57, 58]. Some of the essential properties normally read include crop height, shoot diameter, branches, leaves, leaf area, biomass weight, root development and structure, and threshed weight of soybean/ha.

3.3.1 Leaf area and leaf area index (LAI)

Leaf area is the area confined by a leaf of a plant. It is the total area of the leaf in square meters (m²). Represented by meter square per meter square, LAI is referred to as the substantial quantity of leaf content existing within a confined area occupied by plants, and its unit is denoted as (m²/m²). In geometrical terms, LAI represents the sum of plain side of a leaf area contained in a matured leaf per geographical area of a group of crops. It is generally defined as the ratio of leaf area to land area and is strongly related to crop yield [59, 60, 61, 62, 63]. Thus, the system of assessing development association among plants growing together is referred to as LAI. The expression generally used in approximating leaf area index has been used by Agba et al. [64]:

$$\text{Leaf area index},\mathrm{LAI}=\mathrm{N}\mathrm{x}\mathrm{Y}\mathrm{x}{\mathrm{A}}_{\mathrm{L}}\mathrm{x}{\left({\mathrm{A}}_{\mathrm{P}}\right)}^{-1}$$E1

N = Mean value of plant canopy or leaves

Y = Total number of crops/cultivated land

A_L = Mean value of each leaf area

A_P = Total range of cultivated land

3.3.2 Dry matter (biomass) yield per plant

Dry matter weight (g) of crop is determined at intervals during cultivation. Specific crops are uprooted at random. The uprooted crops are then dried in the oven at 100°C for 72 h. The average weight (g) values of samples selected at random at each plot, which were already dried in an oven, were booked as the appraised crops’ biomass.

3.3.3 Grain yield per hectare (t/ha)

Grain yield per hectare (t/ha) is measured by getting yield per hectare of different units of the cropping area using a weighing balance. The mean value of the various trials is then taken.

3.3.4 Mean relative growth rate (MRGR)

Researchers utilize mean relative growth rates (MRGRs) to compare the growth of seedlings that differ in original size. There are several reasons why this system is applied, which are given as follows: (i) so as to eradicate any development alterations relating to size, (ii) in order to fix crops that are characteristically more feasible, (iii) agglomerated growth of different portions of crops is combined, classes and handling differences are equated and finally (iv) computation of MRGR of intermediate components (root and stem) is unswervingly equivalent to MRGR. The method is normally based on the fact that plant development takes place as percentage of the original magnitude of crop which is constant. This is referred to as the law of compound interest. Researchers make use of this scenario even as the proportionate upsurge fluctuates with growing size, hence the law of variable interest. Examining MRGR of plants is among the various systems applied in associating development alterations arising during trial handlings [65, 66, 67, 68, 69].

The mean relative growth rate has been used to assess plant development caused by various values of fertilizer application on the land, control of weeds, soil manipulation systems, moisture content of soil, soil bulk density, voids and void ratio, load-bearing capacity of soil, soil erodibility, floods and soil chemical properties [70, 71, 72, 73].

According to Paine et al. [69], the MRGR is given by the following expression:

$$\mathit{\text{MRGR}}=\frac{1}{\mathrm{W}}\frac{\mathit{dw}}{\mathit{dt}}=\frac{\mathit{lnW}2-\mathit{lnW}1}{t2-t1}$$E2

Where,

w = weight of oven-dried crop

dw = difference in weight of oven-dried crop

dt = time interlude

ln = ordinary logarithm

w1 ₌ original dry weight of seedlings at initial time, t₁

w2 = concluding weight of dry seedlings at time t₂ and

t2–t1 = period of development in days

Applying this method, Alameda and Villar [32] revealed that a 41% momentous upsurge in relative development rate as seedlings of 17 woody crop species were cultivated under reasonably densified soil with 0.1–1.0 mega pascal in a greenhouse.

4. Modeling growth and yield of crops

Forecasting yields of plants has comprehensive consequences for economic considerations, biology and welfare of humans. Different factors that affect crop production cause the idea of modeling cultivation of plants highly cumbersome. Forecasting the output of cropping activity during agricultural practice possesses a high barrier, since the requirement making interpretations on forthcoming performance is dependent on previous circumstances. Manjula and Djodiltachoumy [74] opined that generation of data is the practice of examining data from various standpoints and abstracting the same into beneficial material. Crop output estimation is a very useful agronomic challenge. Agriculturists normally focus on output immediately after a contemplation of agricultural activities is initiated.

4.1 Estimating crop growth and yield using prior data

In the past, output forecasting was computed by investigating the knowledge of the previous agricultural operation. Sangeetha [75] formulated a method toward forecasting crop output by relying on the knowledge obtained from previous statistics of the agricultural field. Some of these data are consideration of the state of the soil, annual rainfall data, temperature, development of crops and so on. She submitted that the suggested model was found to be more applicable for estimating agricultural output than the previous model.

4.2 Forecasting crop growth and yield using regression model

Odey [76] stated that a regression model was formulated for forecasting the maize development and yield on soil densified by tractor passes. In this estimation, maize yield, Ym, was known in advance, provided the number of tractor wheel passes during operation on the agricultural land was recorded. The study was conducted at the experimental farm land of the Agronomy Department in the Obubra Campus of the Cross River University of Technology, during the planting season of 2016. The experiment was carried out with the intention of knowing how machinery traffic affects properties of soil and development and output of maize planted during the said planting season. Removal of vegetation from the farm land was carried out prior to soil manipulation. Three replications of four different treatments were carried out, adding up to a total of 12 distinct land areas for the experiment. The 12 distinct plots were tagged—[A0], representing zero machinery passes, second, [B10], representing 10 machinery passes, [C20], standing for 20 passes of tractor wheels and [D30] representing, 30 passes of farm machinery wheels. The distinct experimental plot has an area of nine square meter. The agricultural field used for the experiment has a total area of 567 square meters. The 12 experimental plots were arranged using the system—Randomized Complete Block Design [RCBD]. Farm tractor having a rated power of 50 kilowatts (kW) was applied in compacting the experimental plots accordingly. Physical and chemical properties were conducted on the soil samples taken before and after treatments of the plots. Maize (FAMMAX-15) seeds were planted. Data on growth and yield parameters were measured.

As recorded by Odey [76], data were analyzed to obtain the relationship between tractor passes, properties of soil and maize growth and yield. More machinery traffic resulted in higher penetrometer readings, soil bulk densities and reduction in moisture of the soil and soil voids. Machinery traffic leads to an increased positive correlation with soil bulk density and negative correlation with soil voids, moisture of soil and development cum output data of the said crop. Thus, soil bulk densities rose up sharply with increased number of machinery passes on the land, whereas the porosity of soil, crop development and output reduced with more number of machinery wheels on the field. Field area with high machinery traffic of 30 passes yielded the minimum output among the 12 plots. Thus, an average output of 2.56 tons per hectare was realized from [A0], while [B10], [C20] and [D30] had corresponding outputs of 2.11, 1.56 and 1.67 tons per hectare accordingly.

Odey [76] further revealed a mathematical expression for estimating maize output prior to gathering operation as the number of machinery passes was equated with soil bulk density, soil voids, moisture content of soil, crop development and final output, using output data as the dependent variable. Thus, the experimental results yielded the following expression for estimating the output of maize:

$${\mathrm{Y}}_{\text{maize}}=29.34+0.012\mathrm{TOT}–11.5\mathrm{Bd}–0.75\mathrm{Ms}–0.25\mathrm{Ps}+0.03\mathrm{Mh}-0.34\mathrm{Mw}+0.37\mathrm{Ml}$$E3

Where, Y_maize = grain yield, TOT = tractor traffic, Bd = soil bulk density, Ms. = soil moisture, Ps = soil porosity, Mh = maize plant height, Mw = maize stem width and Ml = number of leaves of maize crop.

Odey [76] concluded that the expression mentioned above had an R² and standard error of 0.645 and 0.43, respectively. Hence, the need for controlled tractor traffic on farm lands to control its reversed effects on agricultural land and crop development. The researcher further emphasized tractor traffic below 10 passes on sandy loam fields for favorable maize cultivation.

4.3 Estimating crop development and output using modeling equations

Nkakini and Davies [77] formulated an expression for okra tolerance output to soil densification. The study showed an existence of mutual association between the investigated and okra values obtained from the mathematical expression or model values of okra output in acceptance of densification of soil under different machinery wheels on the various experimental fields that were studied. This mathematical expression revealed that okra development and output significantly improved as a result of imposed compaction due to tractor wheel passes. The said experiment was conducted using a total study area measuring 1656 square meters. Clearing and stumping of the field were carried out. Soil tests were carried out on the randomly collected samples of soil before and after manipulation of soil using tillage machineries. The experiment was laid on a randomized complete block design of four replications. Machinery traffic at i = 0, 5, 10, 15 and 20 per replicate was put in place making a total of 20 subplots using a model SWARAJ 978 FE Tractor. Duly certified okra seeds with minimum percentage germination and purity of 85 and 99% were procured and sown accordingly. Data on growth and yield of maize were used in the model development.

4.4 Finite modeling of growth and yield of okra using different tillage systems

Odey [25] conducted a finite modeling of growth and yield of okra using different tillage systems. The field experiment carried out was aimed at modeling okra growth and yield cultivated using different tillage systems. The experiment used three treatments replicated three times in randomized complete block design (RCBD). These tillage systems that were studied were: [A] Conventional-plowing and harrowing, [B] Conservative tillage system and [C] No till method. The three systems of tillage under study had a multiple of 3. There were nine plots in all. An area of 64 square meters was allocated to each of the nine fields. A space of 2 meters separates each of the plots for tractor maneuvering. Planting of okra at distance, 1 m x 0.8 m, was done. The weeds were removed 3 weeks after cropping and an interval of 2 weeks thereafter until gathering fruits after maturity. An assessment of physiochemical properties was carried out in the laboratory on the soil samples taken before and after planting operation.

Odey [25] further explained that values of okra output were taken and then subjected to analysis on IBM SPSS Statistics Version 21 software. The results revealed reversed correlation among development data, output of okra and bulk density of soil. Whereas soil voids had positive correlation with okra development and output, conventional and conservative tillage had higher okra yield compared to no till method. Finite modeled expression with R² of 0.934 on okra output studying various soil manipulation methods was formulated, showing forecasted results almost equating with actual output. The researcher recommended conventional and conservative tillage systems for enhancement in the production of okra fruits in sandy loam soil. Thus, a detailed explanation on how Odey [25] conducted the research is given below for an understanding of the reader:

4.4.1 Experimental site and location

The site of the experiment was experimental farm land of the Agronomy Department, Cross River University of Technology, Obubra Campus. This experiment was conducted during the planting season of 2017/2018. The location of the site was at longitude 08^o20^’00^″E and latitude 6^o05^’00^”N. The rainfall data at this location were about 500–1070 mm annually, with a soil type, sandy loam and a temperature range of 21–30°C. The vegetation was a rain forest zone.

4.4.2 Land preparation and experimental design

The vegetation was cleared by spraying systemic herbicide using a portable knapsack sprayer, since the land area was predominantly covered with weeds, such as elephant grass, Chromolaena odurata and Centrosema. The dried vegetation was removed after 2 weeks. A randomized complete block design (RCBD) explained earlier was adopted on the field.

4.4.3 Treatments adopted

4.4.3.1 Conventional tillage

Conventional tillage was carried out using Massey Fergusson—MF 435 2WD/4WD tractor with rated power, 50 kW and total weight, 2122 kg which was utilized in plowing and harrowing the plots before planting the seeds.

4.4.3.2 Conservative tillage

Thus, 1120 kg of crop residues was incorporated into the soil per hectare (1120 kg/ha). This was to meet up with the standard 30% of the land area for cropping as specified by ASAE (American Society of Agriculture and Biological Engineers) [22]. Therefore, a total of 7.168 kg of crop remnants was utilized/mixed with each of the 8 m x 8 m plot sizes dedicated for conservative tillage system and harrowed before planting the seeds in accordance with Reddy et al. [78].

4.4.3.3 Zero tillage

In the zero tillage system, the vegetation was cleared and raked. Seeds were planted directly without manipulating the soil, as reiterated by Ruberson and Phatak [16].

4.5 Planting material and germination

Viable seeds of okra (Agwu Early) were soaked for 1 day before planting according to Omran et al. [79]. Then the seeds were planted at 0.02–0.05 m below the soil surface, selecting 2–3 seeds per slit, then, they were later thinned to two stands. The planting distance employed was 1 m x 0.8 m (inter- and intrarow). Germination of grains, growth and yield data were ideal and were determined using the expression after the researcher Agba et al. [64];

$$\text{Germination percentage}=\frac{\text{Number of plants germinated}}{\text{Total number of seeds planted}}x100$$E4

4.6 Replanting of ungerminated seedlings and pruning

Replanting of ungerminated crops from the nursery raised close to the experimental plots was conducted according to Khan et al. [80].

4.7 Weeding and fertilizer application

Manuel weeding using cutlass was carried out 3 weeks after planting. The process was repeated after an interval of every 2 weeks till beginning of harvest. Application of fertilizer, NPK (nitrogen, phosphorus and potassium), 12: 12: 17 was done after 3 weeks of planting using ring method at the rate of 3.2 kg/experimental plot.

4.8 Data collection

4.8.1 Collection of soil samples

Soil samples were collected at random using soil cores before tillage operations and during growth and maturity of crops on the entire land area (1024 m²). The samples were collected at depths of 0–15, 16–30 and 31–45 cm using undisturbed cores. Note that the samples were taken from each plot during the development and fruiting periods at the specified points below the soil surface. Soil samples collected were treated accordingly before laboratory experimentation.

4.8.2 Measurement of growth parameters and yield of okra

At 2 weeks after planting, data on the growth rate, such as plant height, width, number of leaves and flowers, were collected. This operation was repeated every 2 weeks interval till maturity and production. Yield of okra data was gathered every 5 days interval as okra pods (matured green pods) were harvested by using knife and hands.

4.8.3 Soil bulk density and porosity

The soil samples were oven-dried at 100°C for 24 h before determining bulk density and porosity using the technique explained by Black and Hartge [81].

4.9 Analysis

4.9.1 Laboratory and statistical analysis

The laboratory analysis of soil samples was done at the Soil Science Laboratory, University of Agriculture, Makurdi. Hydrometer method, as explained by Gee and Bauder [82], was used for determining the particle size. The pH meter with glass electrode was utilized for determining the pH of soil in water, 1:2 of soil:water ratio. Experimentation with flame photometer, as described by Udo et al. [83], was utilized to determine cation exchange, whereas potassium and sodium, calcium and magnesium were determined in the extract by ethylenediaminetetraacetic acid (EDTA) titration. The dichromate wet-oxidation method, as explained by Nelson and Sommers [84], was used for getting organic matter content. Phosphorus content was realized by the Bray-1 step, as described by the researcher Kuo [85]. The available nitrogen was obtained by the micro-Kjeldahl digestion and distillation system, as put forward by Bremmer [86]. Cation exchange capacity (CEC) was determined according to the method explained by Sumner and Miller [87]. The data obtained on growth and yield parameters of okra during the study were subjected to test and analysis using IBM SPSS Statistics Version 21 software. Among the determined components were correlation, regression and analysis of variance (ANOVA).

4.10 Results and discussion

4.10.1 Soil properties of the experimental site

Table 1 shows the result of pretreatment of soil physical and chemical properties of the study site. Thus, textural class of soil is sandy loam having higher percentage of sand particles among all points below the soil surface taken, viz., 0–15, 16–30 and 31–45 in centimeters and the presence of acidic content was noticed in the different depths. Soil within 0–15 cm depths showed more values of organic matter, followed by points 16–30 and 31–45 accordingly, confirming organic matter decreases with soil depth. The soil available N, P content, cation exchange capacity (CEC), Mg and organic matter were found to be minimal, indicating the inherent low fertility status of tropical soils, as reiterated by Ojeniyi [88].

Depths	% sand	% silt	% clay	Textural class	H2O 1:1	KCl 1:1	Org C%	Org M%	N%	Bray 1 P PPM	Ca mmol/L	Mg mmol/L	K mmol/L	Na mmol/L	CEC
0–15 cm	76.0	12.0	12.0	Sandy loam	5.93	5.11	0.86	1.49	0.092	3.68	3.71	1.58	0.33	0.64	6.50
16–30 cm	75.9	12.9	11.2	Sandy loam	5.91	5.02	0.77	1.33	0.094	3.81	3.69	1.62	0.34	0.70	6.46
31–45 cm	70.2	13.7	16.3	Sandy loam	5.50	4.80	0.66	1.14	0.088	3.9	3.77	1.66	0.30	0.69	6.43
Average	± 3.3	± 0.9	± 2.7		± 0.24	± 0.16	±0.10	±0.18	±0.0	±0.11	±0.04	±0.04	±0.02	±0.03	±0.04

Table 1.

Properties of soil at various depths before treatments of agricultural field.

Source: Field data 2017/2018 cropping seasons [25].

4.10.2 Soil properties as influenced by treatments, growth parameters and yield of okra

Odey [25] further revealed that the structural properties of soil were affected during field experiment (see Tables 2 and 3). Table 2 shows how the different soil manipulation methods influenced physiochemical properties of soil during growth stages of okra. The results indicated that the treatments have a very similar textural class, sandy loam soil, with a higher percentage of sand characteristics showing in the various treatments. Soil pH remained acidic in all the treatments. Organic matter was maximum at 0–15 cm depth in both conventional and conservative tillage than 16–30 cm and 31–45 cm depths apart from zero tillage.

Treatments	Depths	% sand	% silt	% clay	Textural class	H2O 1:1	KCl 1:1	Org C%	Org M%	N%	Bray 1 P PPM	Ca mmol/L	Mg mmol/L	Na mmol/L	Na mmol/L	CEC
A	0–15 cm	71.1	13.1	15.8	Sandy loam	6.17	5.35	0.77	1.33	0.079	3.62	3.58	1.63	0.29	0.68	6.11
	16–30 cm	75.4	11.2	13.4	Sandy loam	6.25	5.15	0.74	1.30	0.078	3.46	3.49	1.61	0.29	0.65	6.18
	31–45 cm	76.2	11.5	12.3	Sandy loam	6.00	5.20	0.71	1.24	0.077	3.11	3.44	1.58	0.29	0.66	6.00
Average		±3.2	±0.85	±2.7		±0.1	±0.1	±0.03	±0.05	±0.0	±0.26	±0.07	±0.03	±0.0	±0.02	±0.09
B	0–15 cm	72.2	13.4	15.4	Sandy loam	5.95	5.00	0.80	1.38	0.077	2.86	3.63	1.57	0.30	0.67	6.20
	16–30 cm	76.3	12.4	11.3	Sandy loam	5.86	5.00	0.77	1.33	0.077	3.01	3.58	1.62	0.27	0.64	6.08
	31–45 cm	69.2	14.2	16.6	Sandy loam	5.00	4.50	0.56	0.97	0.075	3.05	3.80	1.70	0.31	0.70	6.75
Average		±3.6	±0.9	±2.8		±0.5	±0.3	±0.1	±0.2	±0.0	±0.1	±0.1	±0.1	±0.0	±0.03	±0.4
C	0–15 cm	75.4	11.6	13.0	Sandy loam	5.92	5.10	0.72	1.24	0.081	3.77	3.41	1.57	0.26	0.64	5.96
	16–30 cm	77.0	12.1	10.9	Sandy loam	6.11	5.90	0.80	1.38	0.079	3.83	3.51	1.60	0.26	0.66	6.21
	31–45 cm	75.0	11.3	13.7	Sandy loam	6.25	5.33	0.88	1.52	0.075	3.82	3.50	1.62	0.27	0.67	6.22
Average		±1.06	±0.4	±1.5		±0.2	±0.4	±0.1	±0.1	±0.0	±0.03	±0.1	±0.03	±0.01	±0.02	±0.2

Table 2.

Properties of soil as affected by field experiment.

Source: Odey [25].

Treatments/ replicates	Soils Bulk density (DB)	Soil porosity	Height of okra (cm)	Width of okra (cm)	No. of leaves/ stand	No. of flowers/ stand	No. of fruits/ stand	Total yield (kg/plot- 64 m^2)	Total yield (kg/ha)
A1	0.49	0.82	77	19.78	43.2	17.6	13.6	45.6	7125
A2	0.52	0.80	75.8	17.76	44	6.4	8	40.8	6375
A3	0.54	0.80	80.6	23.84	48	4	9.6	48.8	7625
B1	0.46	0.83	88.6	19.12	56.8	16	16.8	62.4	9750
B2	0.54	0.79	56.6	13.02	45.6	14.4	7.2	29.6	4625
B3	0.46	0.83	81.4	25.08	68.8	19.2	19.2	47.2	7375
C1	0.54	0.80	58	14.24	33.6	9.6	15.2	31.2	4875
C2	0.59	0.78	75	16.54	44	8.8	8.0	36.8	5750
C3	0.52	0.80	68.4	14.08	32.8	8.0	5.6	40.8	6375
Average	±0.04	±0.02	±10.68	±4.26	±11.09	±5.37	±4.83	±10.00	±1562.2

Table 3.

Average data for density of soil, voids, development and output of okra.

Source: Odey [25].

On the other hand, different tillage systems did not lead to rise in soil N, P content, cation exchange capacity (CEC), Mg, Ca and Na in the different depths under study. These results correspond with the findings of Brady and Weil [89], which revealed that soil contains minerals, organic matter, air and water; confirming the submission of Carter [90] who reported that soil textural class is made of clay, sand and silt.

Thus, conventional and conservative soil manipulation methods resulted in increased output of the crop than the output from no till method system. These findings concurred with the submission of Lal [91], which reiterated that various soil manipulation methods have effects on okra output.

4.10.3 Analytical results for properties of soil, development and okra output

According to Odey [25], the experimental results in Table 4 revealed the analytical results for properties of soil, development and okra output. There is a strong positive relationship between height, width and leaves of okra, showing the correlation between height and width of okra tending to 1 (1.0), and in agreement to that of leaves and width. The findings are related to the assertion of Ariyo and Akenova [92], which expressed a strong affiliation among the development criteria of crops. Hence, there is a strong positive correlation among okra development and output and soil voids. Thus, as densities of voids were 1.000, tallness, breadth, leaf count, flowering and okra output maintain their stand at 0.5248, 0.4904, 0.6115, 0.6825 and 0.6909 accordingly. Whereas, sturdy adverse correlation was displayed between densities of soil, development and okra output. When densities of soil were 1.000, soil voids were − 1.000, while tallness, leaf count, flowering and okra output stood at −0.5249, −0.4904, −0.6115, −0.6825 and − 0.6909, respectively.

	Bulk density	Porosity	Height	Width	Leaves	Flowers	Total yield
Bulk density	1.000
Porosity	−1.000	1.000
Height	−0.5249	0.5249	1.000
Width	−0.4904	0.4904	0.7707	1.000
Leaves	−0.6115	0.6115	0.6399	0.7520	1.000
Flower	−0.6825	0.6825	0.1731	0.2086	0.5799	1.000
Total yield	−0.6909	0.6909	0.9162	0.6459	0.5731	0.2596	1.000

Table 4.

Analytical values of properties of soil, development and okra output.

4.10.4 Regression of soil properties and growth parameters with yield of okra

Odey [25] reported an analytical work carried out on properties of soil—densification of soil and voids, development data and okra output, with the dependent variable as okra output (see results in Tables 5 and 6). Mathematical expression for okra output per hectare was determined from the analyzed results presented. Hence, predetermined model for okra output on agricultural field experiment conducted by different soil manipulations was inferred.

Parameters		Unstandardized coefficients		Standardized coefficients	T	Sig.	95.0% confidence interval for Beta
		Beta	Std. error	Beta			Lower bound	Upper bound
	(Constant)	-84.64	124.391		−.680	.545	−480.511	311.222
	Bulk density	12.229	47.091	.206	.260	.812	−137.635	162.092
	Porosity	93.963	124.816	.623	.753	.506	−303.257	491.184
	Height	.212	.050	.906	4.264	.024	.054	.370
	No leaves	−.244	.215	−.271	−1.137	.338	−.928	.440
	Treatment	.201	.483	.070	.416	.705	−1.337	1.739

Table 5.

Regression coefficients.

Reliant variable: Okra output Odey [25].

Model	R	R square	Adjusted R square	Std. error of the estimate	Durbin-Watson
1	.966a	.934	.823	1.05255	1.165

Table 6.

Model summary.

a

Predictors: (Constant), Treatment, No. of leaves, Bulk density, Height, Porosity.

b

Dependent variable: Yield of okra Odey [25].

$$Y_o=-84.64+{12.23}_{\mathrm{bd}}+{93.96}_{\mathrm{p}}+{0.21}_{\mathrm{H}}–{0.24}_{\mathrm{N}L}+{0.20}_{\mathrm{T}}$$E5

Where,

Y_o = okra output

Bd = soil bulk density

P = soil porosity

H = okra plant height

NL = numbers of leaves of okra

T = experimental treatment

The model eq. 5 above had a R² value, 0.934. The distinctions among mean values of estimated and observed okra output at 95% confidence level were applied to infer when statistical tools were used. According to Odey [25], t-test results revealed—no important difference at (p > 0.05) between experimented and predicted output of okra recorded when various soil manipulation methods were carried out. It can be inferred that the forecasted output expression has close relation to the actual okra output. Therefore, the model is utilized in forecasting the production of okra in advance for any given agricultural field, provided other data are known. These findings agreed with the assertion of Mulumba and Lal [93], which stated that tillage practice is a management input that influences soil physical characteristics, which in turn touches growth and yield of crops.

Odey [25] concluded that the field study conducted was aimed at modeling okra growth and yield using different tillage systems, with the intention of predicting the output of okra. Field data were obtained and analyzed using appropriate tools. Findings revealed the negative relationship among development data, okra output and soil densities. Whereas, porosity of soil showed positive relationship among development criteria and okra output. Okra output was more in conventional and conservative tillage than in no till system. Modeled equation with R squared, 0.934 on the okra output using different tillage practices was created from the regression analysis, showing forecasted output almost equaled observed okra yield. The output of okra is estimated during cultivation, provided soil physical properties and growth parameters of okra are identified as in Odey et al. [94]. Odey [25] recommended conventional and conservative tillage systems for enhancement in the production of okra on soil with sandy loam texture.

5. Conclusion

Tillage, an integral component of crop production systems, exists with agricultural practice. It affects crop growth and yield. Different field experiments conducted by researchers on the effect of different tillage systems for predicting growth and yield of crops have been discussed. It has been revealed that estimating plant yield has comprehensive inferences for economics, ecology and human welfare. Moreover, a number of factors determining crop productivity make modeling crop production a challenging task. Forecasting crop production is more challenging, requiring making inferences on future performance based on past conditions. Farmers always try to focus on yield as soon as cropping activities commence. Finite model equations for predicting crop growth and yield are better than using farmer’s previous experience on a particular crop. The chapter discussed how different tillage systems including conventional, conservative, reduced, zero tillage or direct seeding, mulch-tillage, ridge-tillage, stale seedbed, minimum tillage and strip-tillage are used in predicting crop growths and yields. Notable models for predicting growth and yield of crops using different tillage systems were highlighted. It is recommended that modern farming should utilize model equations for estimating crop growth and yield using soil physical properties, climate conditions and relevant parameters of appropriate tillage systems for improvement in the production of crops depending on soil types.