For potato cultivation under conventional tillage we grow potato on ridges. Which provides better physical conditions for potato growth. But under zero tillage how planting was done. Please compare soil porosity, bulk density and moisture distribution under both the system for analyzing the data.

Most environmental and agroclimatic factors.

Zero tillage for a root crop like potatoes will be more influenced by soil physical factors such as bulk density, porosity, texture, etc, than other cultural factors. If you have robust data, I will suggest a Step-wise regression analysis of all the factor data and yield. in order to determine their correlations. I believe this may give you some insight into the most and least influential factor to the potato yields.

designe the factorial experiment system (within split-plot) using a completely randomized block design and with three replications.

I think it is possible to check it using statistical methods, for example simple correlation (Pearson's linear correlation or Spearman's correlation rank coefficient depending on the kind of data) between yields (dependent variable -Y) and all management factors (independent variables) or some mutivariate methods, such as multilinear regression and path analysis. And FIRST, it may be useful toprepare scatterplots with particular management factor on X axis and yield on Y axis.

Possible to use statistical softwares (SAS recent versions), however you can use a type of factorial combination in treatment application such as fertilizer, seed rate, variety, etc.

Thank you!

Stochastic frontier analysis you may be considered as an analytical tool.

I suggest you consider two things

1. Correlation analysis between the soil properties (important physico-chemical) and potato yield under both management separately, then with that you would fully confirm which of the properties affect the potato yield most or least

2. Carry out step-wise regression analysis for the prediction of potato yield. The soil properties under both management practices that significantly influenced the potato yield is/are retained in the model.

3. Lastly, it is important to know the number of treatments you have apart from the consideration of the soil properties. In that way, the design of your experiment is very important. .... You mention mulching, irrigation amount, fertiliser application, variety and so on. Please, take caution on how you combine all these factors to form your treatments.

If all these factors are all considered in your experiment, then I will further advice you to carry out Principal Component Analysis in your analysis

Dear M

As for the potato factors I suspect that soil texture and fertility are most important.

Potatoes are well known for Phosphorus response. If I were trying to optimize the potato yield and quality I would make sure wheat was the previous crop and amount of P is optimized.

Potatoes respond to porous welll drained soils so the raised bed is very important.

Since irrigation is so critical also the use of mulch preferably reflective siliver plastic to reduce aphid issues.

Potatoes need to certified virus free seed piece materials.

Beside the soil test tissue testing can be use to fine tune your production environment.

Mustafa, did you try principal component analysis?

I agree with Dheyaa Aljuburi

Dear Mustapha,

First of all, yield reduction means you are comparing zero tillage to conventional tillage. Thus you should have data for both crop management types. Second, do you have an observational or an experimental study? In the former you are measuring passively different possible factors as the ones that you listed whereas in the latter it means you did a trial using an experimental design with the possible influencing factors. However, it is really hard to do an experiment with such high number of factors. Could you please clarify what you did following my questions? How many observations do you have? What is the nature of the factors (quantitative, categorical or binary)? I could help based on the answers to my questions.

The analysis part cannot be explained without knowing how did you conduct the research.

In zero tillage, in my opinion, applied input like fertilizer and compost in addition to variety b/s there is cultivation and earthing up to decrease the compaction as potato is a short root system which can create enough soil with growing conditions.

Potato is famous for being very responsive to Phosphorus and organic matter.

The crop can be very responsive to increase organic nutrition which is complete by using compost amendment.

The use of sandy soils for potato and vegetable production is something at permited adequeate drainage but compromises water availability.

Raw manures have issues for vegetable production of a sanitary nature.

The use of compost is a way of not only providing higher water efficiency but also optimized complete nutrtion.

Compost gives an ability to make inroads in the enriched greenhouse gas environment as the compost is more about to stick around and accumulate in the soil rather than being quickly released to the air.

All nutrition comes from water and a big proportion of water relationship depends on soil organic matter.

You should determine soil physical properties like bulk density, porosity, water distribution pattern etc. You have not mentioned when this experiment was initiated. Because zero tillage gives better results after 3 years or so.

The use of biomass for energy, chemicals, and materials is considered an important alternative to fossil resources (Chum et al. 2011; Harvey and Pilgrim 2011). For biomass to deliver a sizeable contribution, the availability of sufficient sustainable and affordable biomass feedstock is crucial. Assessment studies that have evaluated the current and future global availability of biomass resources show that the largest future potential contribution can come from energy crops grown on various types of land (Hoogwijk et al. 2005; Smeets et al. 2007). The most important land type is surplus agricultural land, which can be released through increased production efficiency of food, animal feed or pasture. There is, however, disagreement about the availability of surplus agricultural land. Key uncertainties in predicting this area of land are technological progress in agricultural production systems and the related increases in crop and livestock yields (Dornburg et al. 2010; Slade et al. 2011; Batidzirai et al. 2012).

Several studies have investigated the effects of yields on the availability of surplus agricultural land and biomass potentials and impacts. For example, van Vuuren et al. (2009) assessed the impact of food crop yield changes on the global woody biomass potential in 2050. They found that an additional yield improvement of 12.5% compared to the baseline scenario resulted in an increase of the biomass potential from 150 to 230 EJ. Erb et al. (2009) found that the biomass potential in 2050 would be 79 EJ year−1 in the case of intermediate agricultural intensification and humane livestock rearing and 105 EJ year−1 in the case of greater intensification of crop and livestock production.1 Dornburg et al. (2010) estimated that improvements in agricultural management could account for 140 EJ year−1 of the total biomass supply potential of 500 EJ year−1 in 2050. Slade et al. (2011) derived from a review study that more than 1 Gha of high yielding agricultural land, equal to about 20% of the global agricultural land area in 2010, could be made available for bioenergy crops in 2050 if food crop yields increase at a higher rate than food demand and if the consumption of livestock products is limited.

The degree of yield improvements also affects the environmental performance of biomass production. Without sufficient improvements in yields, there is a large risk of direct or indirect land use change (DLUC and ILUC, respectively), which can result in high greenhouse gas (GHG) emissions (Searchinger et al. 2008; Laborde 2011). In addition, advances in agricultural production systems may also improve the performance of the agricultural sector as a whole. For example, Tilman et al. (2011) show that there is a significant potential in agriculture to reduce global land clearing, GHG emissions and nitrogen use through improved technology and adaption and transfer of high‐yielding technologies to underyielding regions. Also, Havlík et al. (2014) show that the transition of livestock production toward more efficient systems would significantly decrease livestock‐induced GHG emissions. These emission savings are mainly a result of a reduction in land use change (Havlík et al. 2014).

Models that assess land availability, land use change induced by biomass demand and other impacts of biomass production, such as those used in the studies mentioned above, generally base their crop yield projections on historical developments. Many of these studies also account for (a limited number of) endogenous drivers of future yields. These factors are related to, for example, climate change (Jaggard et al. 2010), crop or land prices (Eickhout et al. 2008; Rosegrant et al. 2008; EPA 2010; Khanna et al. 2011) or management changes like the increased use of fertilizer and other production factors (Eickhout et al. 2008; Beach et al. 2010; Mosnier et al. 2012). This diversity of factors reflects that in reality yield developments depend on numerous factors of various origins (e.g., economic, technological, and ecological). The question arises as to what role these different driving factors play, how they relate to each other and if their impact varies between regions. Moreover, productivity developments in the livestock sector have received much less attention in literature and modeling efforts than agricultural crops – despite the fact that livestock production accounts for 70% of the total agricultural land and one third of the arable land area is used for feed crop production (Steinfeld et al. 2006). For this sector, the lack of insight into the possibilities to increase yields, the rate at which this can be established and the role of different driving factors is even larger.