**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' wellbeing. 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 [39–53]. 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, 43–45, 47, 49, 50, 55–58]. 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 (m<sup>2</sup> ). 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 (m<sup>2</sup> /m<sup>2</sup> ). 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 [59–63]. 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]:

$$\text{[Leaf area index, LAI} = \text{N x Y x A}\_{\text{L}} \ge \text{(A}\_{\text{P}})^{-1} \tag{1}$$

N = Mean value of plant canopy or leaves Y = Total number of crops/cultivated land AL = Mean value of each leaf area AP = 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 *Modeling Growth and Yield of Crops Using Different Tillage Systems DOI: http://dx.doi.org/10.5772/intechopen.113410*

among the various systems applied in associating development alterations arising during trial handlings [65–69].

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 [70–73].

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

$$MRGR = \frac{1}{W} \frac{dw}{dt} = \frac{\ln W 2 - \ln W 1}{t2 - t1} \tag{2}$$

Where,

w = weight of oven-dried crop dw = difference in weight of oven-dried crop dt = time interlude *ln* = ordinary logarithm w1 <sup>=</sup> 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:

$$\text{Y}\_{\text{maize}} = 29.34 + 0.012 \text{ TOT-11.5 Bd-0.75 Ms-0.25 Ps} + 0.03 \text{ Mh} - 0.34 \text{ Mw} + 0.37 \text{ Ml} \tag{3}$$

Where, Ymaize = 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 R<sup>2</sup> 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<sup>o</sup> 20' 00″ E and latitude 6o 05' 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];

Germination percentage <sup>¼</sup> Number of plants germinated Total number of seeds planted *<sup>x</sup>* 100 (4)

#### **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 m<sup>2</sup> ). 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].
