Hi Janna,

I find the solution provided by Lucie Büchi quite seducing. However, this approach might reveal to be tricky if some of the rotations are very innovative (i.e. they include crops for which local references are difficult to find). Moreover, these reference yields will be needed for each crop each year due to the effect of weather conditions, pests, etc. on crop yields. This is the approach taken by the 0pest network in France.

Gross margin are interesting from a farmer's perspective but from a more global perspective, we have to feed the population and the total quantity produced reflects - at least partially - the answer to this challenge. Therefore, in an article (Diversified grain based cropping systems provide long-term weed control while limiting herbicide use and yield losses), we decided to convert the yield of each crop in energy (MJ/ha). Conversion tables for each crop can be found in the feedipedia website. However, this clearly favours crops which produce sugar (potato, sugarbeet, maize...) instead of proteins (fababean, soybean, lentil, chickpea..) or oil (sunflower, oilseed rape).

Hope this helps.

It is useful to start by making an estimate of yield potential at your site using the rainfall data you have collected. Use the following rough guide.

For each mm of growing season rainfall above 100 mm expect 15 (to 20) kg grain ha-1.

Thus for 300 mm growing season rainfall, yield potential is (300-100) x 15 kg ha-1 = 3 (to 4) t ha-1.

This relationship errs on the conservative side and varies somewhat between regions, but try it anyway to indicate roughly how well your treatments are performing. You will notice that it considerably underestimates production in the earlier case study where management was optimized.

If treatments are yielding well below the calculation, it is likely that there is an underlying cause of yield limitation that may not be related to your treatments such as saline subsoils or soil compaction.

Now have a session with collaborating farmers to decide exactly what the data say. You will want to have this assessment session after each season.

Many of the things that should be discussed will be obvious, like how did the break crops fare in their own right; which was the best break crop; were there any real problems associated with the break crops that could be overcome in any repeat study?

Before getting into debating and explaining in detail why particular things happened, a good starting point will be discussion of the cost benefit analysis or gross margins analysis. In a nutshell, this will show whether the sequences were worthwhile and whether further optimization of the sequences is required. An example follows.

Working out gross margins

The calculations in the following table use actual relative prices (with a nominal currency) and actual data collected from a study similar to that proposed as your on-farm experiment. It considers three sequences, wheat-wheat, canola-wheat and peas-wheat, i.e. wheat in the second year of all sequences.

Add your own data to a similar table but use local currency and adjust costs and values of crops to reflect local markets. You might decide to rework the example with local prices.

The example table shows the second year either as a dry season or a wet season. Only the zero N treatment is presented for the dry season as it is often impractical to add N under dry conditions. For the wet season, a 50 kg N ha-1 treatment is shown after wheat and after canola, as a top dressing would be appropriate in such a season. The added N cost is approximately US$50. It is unnecessary to add N after peas.

Check the following conclusions using the data from the table and look for parallels and differences in your own data.

Gross margins calculations

For break crops in year 1, for the wheat crops of the following year (year 2) and for the combined crop sequences, wheat-wheat, canola-wheat and pea-wheat (last column) Year 2 examples are for a dry year, when no N was applied to the wheat crop, and for a wet year. In the wet year 50 kg Na ha-1 was applied after wheat and canola, but not after peas. Price for wheat varies with protein content

• Look at data for year 1 crops in the column labelled 5. The gross margin for peas (green row) was poor (US$250), much less than half that for wheat (US$680, grey) and canola (US$764, yellow).

Taken on that year alone, canola seemed to be a suitable economic alternative for wheat.

• Look lower down the same column for year 2, for the dry season. These crops are all wheat but follow wheat, canola or peas.

Wheat after peas at zero N does very well in economic terms (US$900 versus US$240 for wheat after wheat). Due to carry over of N from the pea crop it yields well (4 t/ha) and has a high value protein content (US$250 versus US$220 per tonne grain). Despite a poor economic performance in year 1, the two-year gross margin for the pea-wheat sequence in column 6 is good (US$1150 versus US$920 for wheat-wheat) or 25 percent better than wheat: wheat.

In your case, this benefit would be reduced if you receive no price advantage for higher protein content grain (12 percent better than wheat: wheat.)

• When year 2 is wet and when 50 N is applied, wheat is susceptible to root disease. Under these conditions the effect of the foregoing ‘break’ crops canola and peas was to ‘break’ the disease cycle. Consequently, yield of wheat after canola or peas was almost three times that after wheat and gross margins over four times greater in that year.

Dear Haider Mahmood, thank you very much for your answer!

Hi Janna, good question we have been thinking about a lot in the past. I don't think there is one unit that can capture this. Cereal units are nice but less suitable because they do not reflect differences in quality parameters and assume cereals as the reference. In the past we used therefore gross margins at the rotational level because this is what matters the most for farmers (it includes the yields but also the costs of producing this yield). Related to yield, we included N balances, N leaching, N2O emissions and weed infestation at the rotational level, see e.g. here Article A cropping system assessment framework—Evaluating effects of...

Article Estimating yield gaps at the cropping system level

We work on this concept in our LegumeGap project https://www.researchgate.net/project/LegumeGap

Best wishes Moritz

Thank you Moritz - these are quite interesting approaches!

Hi Janna,

for a recent study, we used so called 'standard reference yields' which are the yield used in switzerland for fertilisation recommendation, as our base, and then compute for each crop the relative yield, ie farmer yield for the crop divided by standard yield. Then we add up these relative yields to compare rotation yield for different fields, with different rotations.. Not sure it is the best solution but the results seemed consistent..

Article Potential of indicators to unveil the hidden side of croppin...

Hi Janna,

I find the solution provided by Lucie Büchi quite seducing. However, this approach might reveal to be tricky if some of the rotations are very innovative (i.e. they include crops for which local references are difficult to find). Moreover, these reference yields will be needed for each crop each year due to the effect of weather conditions, pests, etc. on crop yields. This is the approach taken by the 0pest network in France.

Gross margin are interesting from a farmer's perspective but from a more global perspective, we have to feed the population and the total quantity produced reflects - at least partially - the answer to this challenge. Therefore, in an article (Diversified grain based cropping systems provide long-term weed control while limiting herbicide use and yield losses), we decided to convert the yield of each crop in energy (MJ/ha). Conversion tables for each crop can be found in the feedipedia website. However, this clearly favours crops which produce sugar (potato, sugarbeet, maize...) instead of proteins (fababean, soybean, lentil, chickpea..) or oil (sunflower, oilseed rape).

Hope this helps.

Dear Lucie, Dear Guillaume,

thanks for your very interesting answers and the article links.

With many thanks,

Janna.

Dear Janna,

i have used CU (Getreideeinheiten in German) extensively and its a nice approach to compare different crops in a rotation. You can use IACS ans LPIS data for a spatial model and incorporate true regional yield levels. Have a look at the two papers, it also contains the most complete review of this CU methods im aware of. Let me know if you have any specific question. Also have a look at the free Deckungsbeitragsrechner by LfL.Preprint Flächenkonkurrenz durch Agroforstwirtschaft? Methodik zur Be...

or also the respective english article: Conference Paper A transferable strategy to reduce competition for food and f...

Please also note the preceding crop value of rape and other legumes for wheat. Oats have a positive effect in the following year for any other cereal crop. Rye or especially simply clover grass is also a suitable healthy crop for problems.

Best, Rico

Very enlightening answers. Thank you.

Dear Janna,

we use FAO model for each crop.

in the following paper you can find how the yield of each crop is estimated using climate and soil data.

"Assessment of the FAO traditional land evaluation methods, A case study: Iranian Land Classification method"

the best